A stem cell abnormality feature recognition method and system based on multi-modal data

Abstract

This invention discloses a method and system for identifying abnormal stem cell features based on multimodal data. It involves intelligent analysis of bioinformatics data and cell feature identification. The method collects multimodal source data of stem cells and performs standardized preprocessing and batch effect correction. It constructs a cross-modal feature encoding network embedded with a prior biological knowledge graph of stem cells to generate fused features. It builds a baseline feature library of normal stem cells to perform initial screening of abnormal features. It identifies core abnormal features through temporal dynamic correlation analysis, verifies the correlation of abnormal phenotypes through causal inference, and stores the identification results in a knowledge base while simultaneously performing incremental iterative optimization of the model. This invention improves the comprehensiveness of stem cell abnormal feature identification by relying on multimodal data fusion and biological prior constraints, and enhances identification accuracy and interpretability by combining temporal analysis and causal verification. The model supports continuous iteration, providing stable and efficient technical support for stem cell quality control and abnormal mechanism analysis.

Description

Technical Field

[0001] This invention relates to the field of intelligent analysis of bioinformatics data and cell feature recognition technology, and in particular to a method and system for identifying abnormal features of stem cells based on multimodal data. Background Technology

[0002] Stem cells have significant applications in regenerative medicine, cell therapy, and drug screening. The stability of the biological state of stem cells directly determines the safety and efficacy of these applications. During in vitro culture, passage, and directed differentiation, stem cells are prone to abnormal changes such as genomic instability, loss of stemness, abnormal differentiation, abnormal apoptosis, and metabolic disorders. These changes are usually accompanied by alterations in multidimensional molecular and phenotypic characteristics, requiring multidimensional data analysis for accurate identification. Current methods for detecting abnormal stem cell characteristics largely rely on single-mic or single-level phenotypic detection. Single-modal data can only reflect local biological changes and cannot fully characterize the overall features of stem cell abnormalities, easily leading to missed or misdiagnosed abnormalities.

[0003] Stem cell multimodal data includes various types of data such as transcriptomics, proteomics, metabolomics, cell imaging, and kinetic parameters. Different modalities exhibit significant differences in their representation, data dimensions, and noise distribution. Traditional data fusion methods often involve simple splicing or linear weighting, failing to fully incorporate prior knowledge of stem cell biology. This results in limited cross-modal feature alignment accuracy and weak biological interpretability of fused features. Existing anomaly identification methods are mostly based on static data analysis, ignoring the temporal dynamic changes in stem cell proliferation and differentiation. They cannot distinguish between random fluctuations and true stable anomalies, making it difficult to identify potential anomalies that only appear at critical differentiation stages. Consequently, the reliability and biological relevance of anomaly identification results are insufficient.

[0004] Current methods for analyzing abnormal stem cell features lack standardized benchmark feature systems and systematic causal association verification mechanisms. Abnormal feature screening often relies on simple statistical tests, failing to establish a comprehensive benchmark for normal stem cells from multiple tissue origins, passages, and differentiation directions. Furthermore, the correlation analysis between abnormal features and abnormal phenotypes is frequently used, lacking interventional causal inference verification and failing to distinguish between core features directly driving abnormalities and secondary features passively accompanying changes. The lack of continuous iterative optimization mechanisms during model training and application prevents the timely integration of new samples and newly discovered biological knowledge into the system. This limits the generalization and continuous optimization capabilities of the overall identification system, making it difficult to meet the demands of basic stem cell research and clinical applications for accurate identification, stable traceability, and reliable annotation of abnormal features. Summary of the Invention

[0005] This invention proposes a method and system for identifying abnormal stem cell features based on multimodal data, in order to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for identifying abnormal features of stem cells based on multimodal data, characterized by comprising the following steps:

[0007] Multimodal source data of stem cell samples were collected, and standardized preprocessing and batch effect correction of the multimodal data were performed to obtain a standardized multimodal dataset that can be used for feature extraction.

[0008] We construct a cross-modal feature encoding network embedded with a prior knowledge graph of stem cell biology, complete deep feature extraction and cross-modal semantic alignment of a standardized multimodal dataset, and generate a biologically consistent multimodal fusion feature representation of stem cells.

[0009] A multimodal baseline feature library of normal stem cells covering different generations, different differentiation directions, and different tissue origins was constructed. Based on the baseline feature library, a multidimensional abnormal feature matching and significance test model was constructed to complete the initial screening of potential abnormal features of stem cell samples and output the initial screening abnormal feature set.

[0010] A time-series dynamic correlation analysis model for the entire cycle of stem cell proliferation and differentiation was constructed. The time-series dynamic change trajectory fitting and covariation correlation analysis of the initial screening abnormal features during stem cell proliferation, passage, and directed differentiation were completed to identify core abnormal features with stable dynamic abnormal patterns.

[0011] Based on core abnormal features, a prediction model for the association between abnormal stem cell phenotypes and interventional causal inference is constructed. The causal association between core abnormal features and abnormal stem cell phenotypes is verified, and the final identification results of abnormal stem cell features and corresponding biological annotation information of abnormal phenotypes are output.

[0012] The identified abnormal features and corresponding annotation information are stored in the stem cell abnormal feature knowledge base, and incremental iterative optimization of the cross-modal feature coding network and the abnormal phenotype association prediction model is completed simultaneously.

[0013] Furthermore, it also includes a bidirectional alignment constraint training step for the cross-modal feature encoding network, with a built-in formula for calculating the cross-modal feature alignment loss:

[0014]

[0015] in The total loss value for cross-modal feature alignment. The total number of training samples. These are the feature distance constraint weight coefficients. The weight coefficients are constrained by the semantic similarity of the features. These are the weight coefficients for prior knowledge constraints, and the sum of all weight coefficients is 1. For the first The first sample Encoded feature vectors for each modality For the first The first sample Encoded feature vectors for each modality For the first The mapping matrix from each mode to the common feature space. This is the Hadamard product operator. The feature mask matrix is ​​constrained by prior knowledge of stem cell biology.

[0016] Furthermore, it also includes the construction and updating of a stem cell biology prior knowledge graph. Specifically, it integrates authoritative database annotation information related to stem cell stemness maintenance, cell cycle regulation, directed differentiation, genome stability, and apoptosis regulation, and constructs a stem cell-specific biological prior knowledge graph that includes gene entities, protein entities, metabolite entities, biological pathway entities, phenotypic entities, and the relationships between entities. At the same time, it continuously updates the knowledge graph and optimizes entity relationships based on the latest research results and new data from the abnormal feature knowledge base.

[0017] Furthermore, the construction steps of the time-series dynamic correlation analysis model for the entire stem cell proliferation and differentiation cycle specifically include: fitting the feature benchmark distribution of all time-series nodes based on the normal stem cell benchmark feature library; and incorporating a formula for quantifying the time-series dynamic significance of abnormal features.

[0018]

[0019] in For the first The temporal dynamic significance score of each anomalous feature, This represents the total number of time-series sampling nodes during the stem cell culture and differentiation process. For the first The feature in the first Sample detection values ​​at each time-series node The first in the normal stem cell benchmark library The feature in the first The mean of each time series node The first in the normal stem cell benchmark library The feature in the first The standard deviation of each time-series node To prevent the smoothing constant from having a denominator of 0, a value of 1e-8 is taken. For the first The biological weight coefficients for each time node are pre-set based on the criticality of stem cell cell cycle and differentiation stage. For the first The feature in the first Biological pathway association coefficients for each time-series node, with values ​​ranging from 0 to 1. For the sample number The variance of each feature across all time-series nodes. The first in the normal stem cell benchmark library The mean and variance of each feature across all time-series nodes.

[0020] Furthermore, the multimodal source data of stem cell samples includes single-cell transcriptome sequencing data, high-content live-cell microscopy imaging data, quantitative proteomics mass spectrometry data, non-targeted metabolomics chromatography data, cell proliferation kinetics time-series data, and cell cycle flow cytometry data. The standardized preprocessing and batch effect correction steps specifically involve using corresponding and adapted preprocessing procedures for different modalities of data to complete data cleaning, missing value imputation, and normalization. At the same time, a batch effect correction method based on an empirical Bayesian framework is used to remove systematic biases caused by different detection batches and different detection platforms.

[0021] Furthermore, the construction steps of the normal stem cell multimodal benchmark feature library are as follows: collect multimodal data of normal stem cell samples from different tissue sources, different passages, different differentiation directions, and different donor sources; extract corresponding multimodal feature representations after standardized preprocessing; construct a multidimensional feature benchmark distribution model; and simultaneously complete the fitting of temporal feature benchmarks for different differentiation stages and different passage numbers to provide a standardized reference benchmark for abnormal feature identification. The benchmark library is continuously updated and its distribution optimized based on newly added normal stem cell sample data.

[0022] Furthermore, the training steps for the cross-modal feature encoding network and the abnormal phenotype association prediction model are as follows: a training set, a validation set, and a test set are constructed using a multimodal dataset of normal stem cells and experimentally validated abnormal stem cell samples. The training and hyperparameter optimization of the model are completed using a five-fold cross-validation method, while the generalization performance of the model is verified using an independent external validation dataset.

[0023] Furthermore, a stem cell abnormality feature recognition system based on multimodal data includes:

[0024] The stem cell multimodal data preprocessing module is used to collect multimodal source data of stem cell samples, complete the standardized preprocessing and batch effect correction of multimodal data, and output a standardized multimodal dataset.

[0025] The stem cell cross-modal feature fusion encoding module is used to construct a cross-modal feature encoding network embedded with a stem cell biological prior knowledge graph, complete the deep feature extraction and cross-modal semantic alignment of a standardized multimodal dataset, and generate a stem cell multimodal fusion feature representation.

[0026] The stem cell abnormality feature screening module is used to construct a multimodal benchmark feature library of normal stem cells, build a multidimensional abnormal feature matching and significance test model based on the benchmark feature library, complete the initial screening of potential abnormal features of stem cell samples, and output the initial screening abnormal feature set.

[0027] The stem cell core abnormal feature time series analysis module is used to construct a time series dynamic correlation analysis model of the entire stem cell proliferation and differentiation cycle, complete the time series dynamic change trajectory fitting and covariation correlation analysis of the initial screening abnormal features, and identify core abnormal features;

[0028] The stem cell abnormal phenotype association verification module is used to construct a stem cell abnormal phenotype association prediction model, complete the causal association verification between core abnormal features and stem cell abnormal phenotypes, and output the final abnormal feature identification results and biological annotation information.

[0029] The model iteration and knowledge base update module is used to store the identified abnormal features and annotation information into the stem cell abnormal feature knowledge base, and to complete the incremental iterative optimization of the relevant models.

[0030] Furthermore, the stem cell cross-modal feature fusion coding module incorporates a multimodal branch coding unit, a common feature space mapping unit, a prior knowledge constraint unit, and a feature alignment training unit. The multimodal branch coding unit uses a deep network structure adapted to the characteristics of the corresponding modal data to complete the extraction of single-modal deep features. The common feature space mapping unit completes the mapping transformation of single-modal features from different modalities to a unified common feature space. The prior knowledge constraint unit completes the weight constraint and masking processing of feature dimensions based on the stem cell biology prior knowledge graph. The feature alignment training unit completes the bidirectional alignment constraint training of cross-modal features.

[0031] Furthermore, the stem cell multimodal data preprocessing module incorporates a multi-source data access unit, a data cleaning and standardization unit, and a batch effect correction unit. The multi-source data access unit supports standardized access and format conversion of various types of stem cell multimodal data, including sequencing data, imaging data, mass spectrometry data, chromatography data, and flow cytometry data. The data cleaning and standardization unit completes missing value filling, outlier removal, and normalization processing for different modal data. The batch effect correction unit completes systematic bias correction for data from multiple batches and platforms.

[0032] Compared with existing technologies, the beneficial effects of this invention are:

[0033] This invention achieves the identification of abnormal features of stem cells based on multimodal data joint analysis, breaking through the information limitations of single-modal data. It can comprehensively characterize abnormal changes in stem cells at the molecular, phenotypic, and dynamic behavioral levels, thereby improving the comprehensiveness and completeness of abnormal feature identification.

[0034] This invention constructs a cross-modal feature encoding network embedded with a prior knowledge graph of stem cell biology, realizing the alignment and deep fusion of biological constraints of multimodal features, improving the biological consistency and interpretability of fused features, enhancing the ability to perceive weak abnormal features, and reducing the interference of noisy features on the recognition results.

[0035] This invention establishes a multimodal baseline feature library for normal stem cells, providing a unified reference standard for the identification of abnormal features. Combined with a time-series dynamic correlation analysis model, it can capture stable abnormal patterns throughout the entire cycle of stem cell proliferation and differentiation, distinguish between random fluctuations and true abnormalities, and improve the accuracy and stability of core abnormal feature identification.

[0036] This invention employs an abnormal phenotype association prediction model that integrates Bayesian networks and interventional causal inference to verify the causal association between abnormal features and abnormal phenotypes, clarify the core driving features and biological significance, and improve the reliability of the identification results and the accuracy of the annotation.

[0037] This invention constructs a knowledge base of abnormal stem cell features and supports incremental iterative optimization of the model. It can continuously incorporate new samples and new biological knowledge, improve the system's generalization ability and long-term applicability, and provide stable and efficient intelligent analysis support for stem cell quality control, abnormal mechanism analysis, and safety application assessment. It promotes the standardization, intelligence, and systematization of abnormal feature identification in stem cell research and application. Attached Figure Description

[0038] Figure 1 This is a schematic block diagram of a stem cell abnormality feature identification method and system based on multimodal data proposed in this invention;

[0039] Figure 2 Flowchart for standardization and batch effect correction of multi-source heterogeneous data;

[0040] Figure 3 Encoding logic graph for knowledge graph embedding and cross-modal feature fusion;

[0041] Figure 4 Flowchart for constructing the benchmark feature library and initial screening of abnormal features;

[0042] Figure 5 Flowchart for full-cycle time-series dynamic trajectory fitting and feature analysis;

[0043] Figure 6 Output a closed-loop graph for causal inference verification and abnormal phenotypic annotation. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0046] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0047] Reference Figures 1 to 6 A method for identifying abnormal features of stem cells based on multimodal data.

[0048] Multimodal source data of stem cell samples were collected, and standardized preprocessing and batch effect correction of the multimodal data were performed to obtain a standardized multimodal dataset that can be used for feature extraction.

[0049] We construct a cross-modal feature encoding network embedded with a prior knowledge graph of stem cell biology, complete deep feature extraction and cross-modal semantic alignment of a standardized multimodal dataset, and generate a biologically consistent multimodal fusion feature representation of stem cells.

[0050] A multimodal baseline feature library of normal stem cells covering different generations, different differentiation directions, and different tissue origins was constructed. Based on the baseline feature library, a multidimensional abnormal feature matching and significance test model was constructed to complete the initial screening of potential abnormal features of stem cell samples and output the initial screening abnormal feature set.

[0051] A time-series dynamic correlation analysis model for the entire cycle of stem cell proliferation and differentiation was constructed. The time-series dynamic change trajectory fitting and covariation correlation analysis of the initial screening abnormal features during stem cell proliferation, passage, and directed differentiation were completed to identify core abnormal features with stable dynamic abnormal patterns.

[0052] Based on core abnormal features, a prediction model for the association between abnormal stem cell phenotypes and interventional causal inference is constructed. The causal association between core abnormal features and abnormal stem cell phenotypes is verified, and the final identification results of abnormal stem cell features and corresponding biological annotation information of abnormal phenotypes are output.

[0053] The identified abnormal features and corresponding annotation information are stored in the stem cell abnormal feature knowledge base, and incremental iterative optimization of the cross-modal feature coding network and the abnormal phenotype association prediction model is completed simultaneously.

[0054] This invention also includes a bidirectional alignment constraint training step for the cross-modal feature encoding network, with a built-in formula for calculating the cross-modal feature alignment loss:

[0055]

[0056] in The total loss value for cross-modal feature alignment. The total number of training samples. These are the feature distance constraint weight coefficients. The weight coefficients are constrained by the semantic similarity of the features. These are the weight coefficients for prior knowledge constraints, and the sum of all weight coefficients is 1. For the first The first sample Encoded feature vectors for each modality For the first The first sample Encoded feature vectors for each modality For the first The mapping matrix from each mode to the common feature space. This is the Hadamard product operator. This is a feature mask matrix constrained by prior knowledge of stem cell biology. The elements in the matrix take values ​​of 0 or 1, corresponding to whether the feature dimension belongs to the core biological pathway of stem cells. By calculating the loss function of multi-dimensional constraints, accurate alignment of data features of different modalities can be achieved in the common feature space. At the same time, the alignment effect of stem cell core biological features is enhanced by the prior knowledge mask matrix, avoiding meaningless noise features from dominating the feature alignment process, and improving the biological interpretability of multi-modal fusion feature representation and the sensitivity of identifying abnormal stem cell features.

[0057] This invention also includes the construction and updating of a stem cell biological prior knowledge graph. Specifically, it integrates authoritative database annotation information related to stem cell stemness maintenance, cell cycle regulation, directed differentiation, genome stability, and apoptosis regulation to construct a stem cell-specific biological prior knowledge graph containing gene entities, protein entities, metabolite entities, biological pathway entities, phenotypic entities, and the relationships between entities. At the same time, based on the latest research results and new data from the abnormal feature knowledge base, it continuously updates the knowledge graph and optimizes the entity relationships, providing biological constraint boundaries for cross-modal feature encoding networks and providing standardized reference for the biological annotation of abnormal features.

[0058] In this invention, the construction steps of the time-series dynamic correlation analysis model for the entire cycle of stem cell proliferation and differentiation specifically include: fitting the feature benchmark distribution of all time-series nodes based on a normal stem cell benchmark feature library; and incorporating a built-in formula for quantifying the time-series dynamic significance of abnormal features.

[0059]

[0060] in For the first The temporal dynamic significance score of each anomalous feature, This represents the total number of time-series sampling nodes during the stem cell culture and differentiation process. For the first The feature in the first Sample detection values ​​at each time-series node The first in the normal stem cell benchmark library The feature in the first The mean of each time series node The first in the normal stem cell benchmark library The feature in the first The standard deviation of each time-series node To prevent the smoothing constant from having a denominator of 0, a value of 1e-8 is taken. For the first The biological weight coefficients for each time node are pre-set based on the criticality of stem cell cell cycle and differentiation stage. For the first The feature in the first Biological pathway association coefficients for each time-series node, with values ​​ranging from 0 to 1. For the sample number The variance of each feature across all time-series nodes. The first in the normal stem cell benchmark library The mean and variance of each feature across all time-series nodes, calculated through cumulative deviation and fluctuation quantification along the time-series dimension, can accurately identify features with significant dynamic abnormalities during stem cell proliferation and differentiation, eliminating interference from static random fluctuations. Furthermore, by combining time-series node weights and pathway correlation coefficients, the significance of abnormal features related to key stem cell differentiation stages and core biological pathways is enhanced, thereby improving the accuracy and biological relevance of core abnormal feature identification.

[0061] In this invention, the multimodal source data of stem cell samples includes single-cell transcriptome sequencing data, high-content live-cell microscopic imaging data, quantitative proteome mass spectrometry data, non-targeted metabolome chromatography data, cell proliferation kinetics time-series data, and cell cycle flow cytometry detection data. The standardized preprocessing and batch effect correction steps specifically involve using corresponding and adapted preprocessing procedures for different modal data to complete data cleaning, missing value imputation, and normalization. At the same time, a batch effect correction method based on an empirical Bayesian framework is used to remove systematic biases caused by different detection batches and different detection platforms, ensuring the comparability and consistency of multimodal data.

[0062] In this invention, the construction steps of the normal stem cell multimodal benchmark feature library specifically involve collecting multimodal data of normal stem cell samples from different tissue sources, different passages, different differentiation directions, and different donor sources; extracting corresponding multimodal feature representations after standardized preprocessing; constructing a multidimensional feature benchmark distribution model; and simultaneously fitting temporal feature benchmarks for different differentiation stages and different passage numbers to provide a standardized reference benchmark for abnormal feature identification. The benchmark library can be continuously updated and its distribution optimized based on newly added normal stem cell sample data.

[0063] In this invention, the training steps of the cross-modal feature encoding network and the abnormal phenotype association prediction model are as follows: a training set, a validation set, and a test set are constructed using a multimodal dataset of normal stem cells and experimentally verified abnormal stem cell samples. The training and hyperparameter optimization of the model are completed using a five-fold cross-validation method. At the same time, an independent external validation dataset is used to verify the generalization performance of the model, ensuring that the model has stable performance and generalization ability for identifying abnormal features of stem cell samples from different sources and of different types.

[0064] This invention provides a stem cell abnormality feature recognition system based on multimodal data, applicable to a stem cell abnormality feature recognition method based on multimodal data, comprising:

[0065] The stem cell multimodal data preprocessing module is used to collect multimodal source data of stem cell samples, complete the standardized preprocessing and batch effect correction of multimodal data, and output a standardized multimodal dataset.

[0066] The stem cell cross-modal feature fusion encoding module is used to construct a cross-modal feature encoding network embedded with a stem cell biological prior knowledge graph, complete the deep feature extraction and cross-modal semantic alignment of a standardized multimodal dataset, and generate a stem cell multimodal fusion feature representation.

[0067] The stem cell abnormality feature screening module is used to construct a multimodal benchmark feature library of normal stem cells, build a multidimensional abnormal feature matching and significance test model based on the benchmark feature library, complete the initial screening of potential abnormal features of stem cell samples, and output the initial screening abnormal feature set.

[0068] The stem cell core abnormal feature time series analysis module is used to construct a time series dynamic correlation analysis model of the entire stem cell proliferation and differentiation cycle, complete the time series dynamic change trajectory fitting and covariation correlation analysis of the initial screening abnormal features, and identify core abnormal features;

[0069] The stem cell abnormal phenotype association verification module is used to construct a stem cell abnormal phenotype association prediction model, complete the causal association verification between core abnormal features and stem cell abnormal phenotypes, and output the final abnormal feature identification results and biological annotation information.

[0070] The model iteration and knowledge base update module is used to store the identified abnormal features and annotation information into the stem cell abnormal feature knowledge base, and to complete the incremental iterative optimization of the relevant models.

[0071] In this invention, the stem cell cross-modal feature fusion coding module incorporates a multimodal branch coding unit, a common feature space mapping unit, a prior knowledge constraint unit, and a feature alignment training unit. The multimodal branch coding unit uses a deep network structure adapted to the characteristics of the corresponding modality data to complete the extraction of single-modal deep features. The common feature space mapping unit completes the mapping transformation of single-modal features from different modalities to a unified common feature space. The prior knowledge constraint unit completes the weight constraint and masking processing of feature dimensions based on the stem cell biological prior knowledge graph. The feature alignment training unit completes the bidirectional alignment constraint training of cross-modal features, ensuring that the generated multimodal fusion feature representation has biological consistency and anomaly detection sensitivity.

[0072] In this invention, the stem cell multimodal data preprocessing module incorporates a multi-source data access unit, a data cleaning and standardization unit, and a batch effect correction unit. The multi-source data access unit supports the standardized access and format conversion of various types of stem cell multimodal data, including sequencing data, imaging data, mass spectrometry data, chromatography data, and flow cytometry data. The data cleaning and standardization unit completes missing value filling, outlier removal, and normalization processing for different modal data. The batch effect correction unit completes the systematic deviation correction of data from multiple batches and platforms, ensuring the quality and comparability of the output standardized multimodal dataset.

[0073] I. Cross-modal feature encoding network embedded in stem cell biology prior knowledge graph

[0074] 1.1 Overall Network Architecture and Branch Unit Design

[0075] This network is an end-to-end coding architecture with multiple branches and a single common space. It has four core units: multimodal branch coding unit, common feature space mapping unit, prior knowledge constraint unit, and feature alignment training unit. The output features of all branches are ultimately mapped to a common feature space of the same dimension, ensuring cross-modal semantic consistency.

[0076] For six types of stem cell multimodal source data, each branch coding unit adopts a reproducible network structure that is fully adapted to the characteristics of the modal data. The specific design is as follows:

[0077] 1. Single-cell transcriptome sequencing data branch: The Transformer encoder architecture is adopted. The input is the standardized gene expression matrix with the dimension being the number of cells multiplied by the number of genes. The embedding dimension is set to 256, the number of attention heads is 8, the number of encoder layers is 3, the activation function is GELU, the layer normalization smoothing constant is 1e-6, and the output single-modality feature vector dimension is fixed at 256.

[0078] 2. High-content live-cell microscopy imaging data branch: The ResNet18 backbone architecture is adopted. The input is a standardized 3-channel microscopic image with a size of 256×256. The top fully connected layer is removed, and one adaptive average pooling layer and two fully connected layers are added. The hidden layer dimensions are 512 and 256, respectively. The activation function is ReLU, and the output single-modal feature vector dimension is fixed at 256.

[0079] 3. Quantitative proteomics and non-targeted metabolomics chromatographic data branch: A one-dimensional CNN architecture is adopted. The input is a standardized molecular abundance matrix. Three consecutive convolutional blocks are set with kernel sizes of 7, 5 and 3, and the number of output channels are 128, 256 and 256, respectively. The stride is 1 for each block. Each convolutional block is followed by a batch normalization layer and a max pooling layer. Finally, a single-modality feature vector is output through a global average pooling layer with a fixed dimension of 256.

[0080] 4. Cell proliferation dynamics time-series data and cell cycle flow cytometry detection data branch: A bidirectional LSTM architecture is adopted. The input is a standardized time-series feature matrix. The hidden layer dimension is set to 128, the number of recurrent layers is 2, the dropout rate is 0.2, followed by 1 fully connected layer. The output single-modal feature vector dimension is fixed at 256.

[0081] 5. Common Feature Space Mapping Unit: The single-modal feature vector of each modality is mapped to a unified common feature space through a learnable linear mapping matrix Wm. The mapping matrix has a dimension of 256×256, and the feature dimension is unified to 256 after mapping, ensuring the dimensional consistency and spatial comparability of features of different modalities.

[0082] 1.2 Construction and Embedding of a Prior Knowledge Graph in Stem Cell Biology

[0083] 1. Knowledge Graph Construction Standards: Integrate authoritative information on gene annotations from the Ensembl database, protein annotations from the Uniprot database, biological pathway annotations from the KEGG and Reactome databases, and cell phenotype annotations from the Cell Ontology database to construct a stem cell-specific biological prior knowledge graph;

[0084] The entity types include five major categories: gene entities, protein entities, metabolite entities, biological pathway entities, and cell phenotype entities. Among them, gene entities cover no less than 2,000 genes related to the core functions of stem cells, covering five core modules: stemness maintenance, cell cycle regulation, directed differentiation, genome stability, and apoptosis regulation.

[0085] The associations are categorized into four main types: gene-protein transcription and translation relationships, gene-pathway regulation relationships, protein-metabolite catalytic relationships, and pathway-phenotype causal relationships. The association weights are set based on the confidence level of literature reports and the database annotation level, with values ​​ranging from 0 to 1.

[0086] The update rule is to update the graph once every quarter based on the latest authoritative research results and newly added verification data from the abnormal feature knowledge base, and optimize the entity association relationships and weights.

[0087] 2. Prior knowledge embedding and feature mask matrix M generation:

[0088] Based on the constructed knowledge graph, feature dimensions directly related to the core biological functions of stem cells are selected to generate a feature mask matrix M. M is a diagonal matrix with dimensions completely consistent with the dimensions of the common feature space, i.e., 256×256. The rules for the values ​​of the diagonal elements are as follows: if the corresponding feature dimension maps to an entity related to the core biological pathway in the knowledge graph, the value is 1; if the corresponding feature dimension is a noise feature related to a non-core pathway, the value is 0. The proportion of feature dimensions related to the core pathway is not less than 60% of the total dimensions to ensure the binding effect of prior knowledge on feature encoding.

[0089] 1.3 Complete Implementation of Cross-Modal Feature Alignment Loss Function

[0090] 1. Loss calculation rule: The total loss for cross-modal feature alignment is Lalign. During training, bidirectional alignment constraint calculation is performed for each modal pair. The total training loss is the average alignment loss of all modal pairs.

[0091] 2. Weight coefficient settings: α is the feature distance constraint weight coefficient, with a value range of 0.2-0.4 and a default value of 0.3; β is the feature semantic similarity constraint weight coefficient, with a value range of 0.3-0.5 and a default value of 0.4; γ is the prior knowledge constraint weight coefficient, with a value range of 0.2-0.4 and a default value of 0.3; α+β+γ=1 is satisfied, and the values ​​can be fine-tuned within the range according to the number of modalities and data types;

[0092] 3. Operational details: The Hadamard product is an element-wise multiplication of the corresponding matrix elements; the mapping matrix Wm is updated synchronously with the backpropagation of the network during training; the smoothing constant ε is fixed at 1e-8 to avoid the denominator being 0; the feature vectors are all L2 normalized vectors to ensure the stability of similarity calculation.

[0093] 1.4 Network Training and Hyperparameter Optimization Methods

[0094] 1. Training dataset construction: A multimodal dataset consisting of no less than 1,000 normal stem cell samples and no less than 300 abnormal stem cell samples validated by the gold standard, covering typical abnormal phenotypes such as genomic instability, loss of stemness, abnormal differentiation, and abnormal apoptosis, was randomly divided into training set, validation set, and test set in a ratio of 7:2:1.

[0095] 2. Training strategy: Five-fold cross-validation is used to complete model training and hyperparameter optimization. The optimizer is AdamW, with an initial learning rate of 1e-4, a weight decay of 1e-5, a batch size of 32, and a maximum number of iterations of 1000. An early stopping mechanism is set up: when the validation set loss does not decrease for 20 consecutive iterations, training is stopped, and the optimal model weights with the lowest validation set loss are saved.

[0096] 3. Generalization performance verification: Use no less than 200 independent external validation datasets, stem cell samples from different detection platforms, different donor sources, and different tissue sources to complete the generalization performance verification. The acceptance criteria are: cross-modal feature alignment consistency of no less than 95% and abnormal feature recognition accuracy of no less than 90%.

[0097] II. Multidimensional Anomaly Feature Matching and Significance Testing Model

[0098] 2.1 Specifications for Constructing a Multimodal Benchmark Feature Library for Normal Stem Cells

[0099] 1. Gold Standard for Sample Inclusion: Normal stem cell samples included in the benchmark bank must simultaneously meet the following requirements: cell viability not less than 95%, normal G-banding karyotype analysis, and no pathogenic genomic copy number variations; expression of stem cell markers must meet the industry gold standard for the corresponding stem cell type, such as a positive rate of CD73, CD90, and CD105 of mesenchymal stem cells not less than 95%, and a negative rate of CD34 and CD45 not higher than 2%; trilineage differentiation potential must meet the quality control standards for human mesenchymal stem cells; and the samples must be free from mycoplasma, bacteria, fungi, and endotoxin contamination.

[0100] 2. Sample coverage dimensions: The samples must fully cover normal stem cell samples from different tissue sources, different passages, different differentiation directions, and different donor sources. Tissue sources include umbilical cord, bone marrow, fat, dental pulp, etc.; passages cover P3-P15; differentiation directions include osteoblasts, adipocytes, chondrocytes, myocardiocytes, nerves, etc.; donor sources cover individuals aged 18-60 years; male-to-female ratio is 1:1; and the total sample size is no less than 1000 cases.

[0101] 3. Benchmark Distribution Fitting Rules: Standardization preprocessing and feature extraction are performed on the multimodal data of all included samples. For each feature dimension, the probability density distribution of the fitted feature is estimated using kernel density estimation to construct a feature benchmark distribution model for each dimension. At the same time, for time-series nodes with different generations and different differentiation stages, time-series feature benchmark fitting is completed, and the mean μ_ref, standard deviation σ_ref, and 95% confidence interval of the feature corresponding to each time-series node are output.

[0102] 4. Benchmark library update rules: For every 100 new normal stem cell samples that meet the gold criteria for inclusion, the benchmark library shall be updated once, and the feature benchmark distribution and time-series benchmark shall be refitted. After the update, the model performance shall be validated to ensure that the anomaly identification accuracy decreases by no more than 2% compared with that before the update.

[0103] 2.2 Implementation Methods for Anomaly Feature Matching and Significance Testing

[0104] 1. Multi-dimensional feature deviation calculation: The multi-modal fusion features of the sample to be tested are matched with the feature benchmark distribution of the corresponding dimension and time node in the benchmark feature library, and the standardized deviation value of each feature dimension is calculated. The formula is as follows:

[0105] ;

[0106] in, Let d be the detection value of the d-th feature of the sample to be tested. , The mean and standard deviation of the corresponding features in the benchmark library. =1e-8 is the smoothing constant;

[0107] 2. Significance test rules: For each feature dimension, a two-tailed one-sample t-test was used to complete the statistical significance analysis. The p-values ​​obtained from the test were corrected using the Benjamini-Hochberg method for multiple tests to control the false positive rate.

[0108] 3. Initial screening anomaly feature set output criteria: simultaneously satisfying... Features with a deviation of ≥2, meaning a deviation from the baseline mean of more than 2 standard deviations and a corrected P < 0.05, are included in the initial screening abnormal feature set; while retaining Features with a value ≥1.5 and a corrected P < 0.05 were selected as candidate anomalous features for subsequent time series analysis verification.

[0109] III. Time-Sequence Dynamic Correlation Analysis Model of Stem Cell Proliferation and Differentiation Cycle

[0110] 3.1 Overall Model Architecture and Timing Node Settings

[0111] 1. Time-series sampling node specifications: For the stem cell proliferation and passage process, the time-series sampling node is the end point of each generation of culture; for the stem cell directed differentiation process, the time-series sampling nodes cover four core stages: undifferentiated stage, lineage specialization stage, progenitor cell stage, and mature cell stage. At least three sampling time points are set for each stage, and the total number of time-series sampling nodes T≥6.

[0112] 2. Core execution process: For each feature in the initial screening of abnormal feature set, complete the dynamic change trajectory fitting of the entire time series nodes, calculate the time series dynamic significance score Sd, complete the covariance correlation analysis between features, and finally screen the core abnormal features with stable dynamic abnormal patterns.

[0113] 3.2 Implementation of Temporal Dynamic Trajectory Fitting and Covariance Correlation Analysis

[0114] 1. Dynamic trajectory fitting method: The local weighted regression method is used to fit the trajectory of the full time-series node detection value of each initially screened abnormal feature. The smoothing parameter span is fixed at 0.75, and the fitted time-series dynamic change curve is output. At the same time, the time-series benchmark mean of the corresponding feature in the benchmark library is synchronously fitted to generate the normal benchmark time-series curve.

[0115] 2. Covariance Association Analysis Rules: Pearson correlation analysis is used to calculate the temporal covariance association between different anomalous features. Features with an absolute correlation coefficient ≥ 0.8 and P < 0.05 are classified into the same covariance feature module for subsequent causal association analysis.

[0116] 3.3 Complete Implementation of Time-Series Dynamic Significance Scoring

[0117] 1. Detailed settings for formula parameters:

[0118] Biological weight coefficients of time-series nodes Based on the criticality of stem cell cycle and differentiation stages, the value ranges from 0.5 to 2.0; among them, the high-risk aging stage above P10 in stem cell passage is defined. =1.5; Key nodes in the lineage specialization phase and precursor cell phase during directed differentiation. =2.0, undifferentiated stage, mature cell stage =1.0; all time-series nodes The mean is 1.0;

[0119] Biological pathway correlation coefficient Based on the correlation between feature-corresponding dimensions and core biological pathways of stem cells, if features are mapped to core pathways in the knowledge graph related to stem cell maintenance, differentiation regulation, and genome stability, =1.0; if mapped to a non-core path, =0.5; if it is a feature without clear biological annotation, =0.2;

[0120] Smoothing constant The value is fixed at 1e-8 to avoid a denominator of 0;

[0121] For the sample to be tested The variance of the full-time node detection values ​​of each feature The variance of the mean of the corresponding feature nodes across all time series in the benchmark library;

[0122] 2. Criteria for determining core anomalies: Time-series dynamic significance score Features with a value ≥3.0 are directly identified as core abnormal features with stable dynamic abnormal patterns; Features with a value between 1.5 and 3.0, if belonging to the core covariant feature module, are simultaneously included in the core abnormal feature set; Features with a value less than 1.5 are judged to be random fluctuation features and are therefore eliminated.

[0123] IV. A Predictive Model for Stem Cell Abnormal Phenotypes Integrating Bayesian Networks and Interventional Causal Inference

[0124] This section fully discloses the model architecture, Bayesian network construction method, causal inference implementation logic, and causal association verification standards.

[0125] 4.1 Overall Model Architecture

[0126] The model consists of three core units: a Bayesian network structure learning module, an interventional causal inference module, and an abnormal phenotype annotation module. The input is a core abnormal feature set, and the output is the abnormal feature identification results verified by causality and biological annotation information.

[0127] 4.2 Bayesian Network Construction and Parameter Learning

[0128] 1. Network Node Definition: Network nodes are divided into three categories, forming a complete feature-pathway-phenotype association link: Feature nodes, corresponding to each feature dimension of the core abnormal feature set; Pathway nodes, corresponding to the core biological pathway entities of stem cells in the knowledge graph; Phenotype nodes, corresponding to abnormal stem cell phenotypes, including six categories: genomic instability, loss of stemness, abnormal differentiation potential, abnormal proliferation kinetics, abnormal apoptosis, and metabolic disorders.

[0129] 2. Structure learning method: A Bayesian network structure learning method based on scoring-search is adopted. The scoring function adopts the Bayesian information criterion, and the search algorithm adopts the hill climbing algorithm. The network structure learning is completed based on the gold standard labeled data of the stem cell abnormality feature knowledge base, and a directed acyclic graph of feature-pathway-phenotype is constructed.

[0130] 3. Parameter learning method: The maximum likelihood estimation method is used to learn the conditional probability table parameters of the Bayesian network and output the conditional probability of each feature node with respect to the path node and phenotypic node.

[0131] 4.3 Complete Implementation of Interventional Causal Inference

[0132] 1. Causal Inference Framework: The Do calculus framework is used to complete the interventional causal inference. For each core abnormal feature, an intervention variable Do (X=x) is set, where X is the target abnormal feature and x is the abnormal value of the feature. The average treatment effect ATE of the abnormal phenotype Y after intervention is calculated. The formula for calculating ATE is:

[0133] ;

[0134] Where X=1 represents abnormal features and X=0 represents normal features;

[0135] 2. Confounding factor control: Based on the stem cell prior knowledge graph, confounding variables that are related to both characteristics and phenotypes are screened, including cell passage, donor age, and culture conditions. The backdoor adjustment criterion is used to control confounding factors, eliminate confounding bias, and ensure the accuracy of causal effect calculation.

[0136] 3. Causal association verification criteria: When ATE ≥ 0.3 and the 95% confidence interval does not contain 0, the core abnormal feature is determined to have a significant causal association with the corresponding abnormal phenotype and is a driving feature of the abnormal phenotype; if ATE < 0.3 or the confidence interval contains 0, it is determined to be a co-occurring abnormal feature, which is only used for biological annotation and is not included in the core driving abnormal features.

[0137] 4.4 Rules for Outputting Biological Annotations for Aberrant Phenotypes

[0138] For core driver anomalous features verified by causality, the system matches them with the stem cell biology prior knowledge graph and automatically outputs standardized annotation information, including: basic information on the genes, proteins, and metabolites corresponding to the features; the associated core biological pathways and regulatory relationships; the corresponding anomalous phenotype type and causal effect value; authoritative database annotations and reported research evidence, forming a complete anomalous feature identification report.

[0139] V. Model Incremental Iterative Optimization Mechanism

[0140] 5.1 Incremental Iteration Triggering Conditions

[0141] Incremental iterative optimization of the model is initiated when any of the following conditions are met:

[0142] 1. The abnormal feature knowledge base will be updated with no fewer than 50 experimentally verified abnormal stem cell sample data.

[0143] 2. The stem cell biology prior knowledge graph has undergone a major update, adding core pathways or entity relationships;

[0144] 3. In practical applications, the anomaly detection accuracy of the model decreases by no less than 5% compared to the benchmark.

[0145] 4. Added modal data type access to the system.

[0146] 5.2 Incremental Iterative Implementation Method

[0147] 1. Incremental dataset construction: The newly added gold standard validated normal and abnormal stem cell multimodal dataset is used as the incremental training set, and 10% of the core samples in the original training set are retained as the memory set to avoid catastrophic forgetting of the model;

[0148] 2. Incremental training strategy: The low-rank adaptation incremental fine-tuning method is adopted. The weights of the cross-modal feature encoding network and the backbone network of the causal inference model are frozen. Only the low-rank matrices of the attention layer and fully connected layer of the network are fine-tuned. The learning rate is set to 1e-5, the batch size is 16, the maximum number of iterations is 200, and the early stopping mechanism is the same as that of the initial training.

[0149] 3. Full update rule: When the cumulative number of new samples is no less than 50% of the original training set sample size, perform a full training, retrain the model and optimize the hyperparameters, and update the backbone weights of the model.

[0150] 5.3 Model Validation Criteria After Iteration

[0151] After the incremental iteration is completed, the model must meet the following acceptance criteria before it can be updated and deployed:

[0152] 1. The anomaly detection accuracy of the original test set decreased by no more than 2%;

[0153] 2. The anomaly detection accuracy of the newly added sample set is no less than 90%;

[0154] 3. Cross-modal feature alignment consistency is not less than 95%.

[0155] The following two examples further illustrate the specific implementation of this system:

[0156] Example 1

[0157] Implementation of Abnormal Feature Identification in Human Umbilical Cord Mesenchymal Stem Cell In Vitro Passage Culture

[0158] This embodiment focuses on identifying multimodal abnormal features during long-term in vitro passage culture of human umbilical cord mesenchymal stem cells. Samples were selected from third to twelfth generation stem cells from the same tissue source and donor, with three parallel samples per generation. The identification targets included abnormalities in genomic stability, stem cell expression, differentiation potential, proliferation kinetics, and metabolic patterns. This embodiment fully covers all technical solutions of the method and system, and all functions and technical designs are consistent with the scope of the invention, with no technical content exceeding the intended scope.

[0159] This embodiment implements a stem cell abnormality feature identification method based on multimodal data, relying on a corresponding identification system to perform the entire process. First, a multimodal source data collection module for the target sample is used, specifically including single-cell transcriptome sequencing data, high-content live-cell microscopy imaging data, quantitative proteomics mass spectrometry data, non-targeted metabolomics chromatography data, cell proliferation kinetics time-series data, and cell cycle flow cytometry data. Adaptive preprocessing is performed on each type of data, including data cleaning, missing value imputation, and normalization. An empirical Bayesian framework is used to correct batch effects, removing systematic biases caused by different detection batches and detection platforms, and outputting a standardized multimodal dataset.

[0160] A cross-modal feature encoding network embedded with a stem cell biological prior knowledge graph is constructed using a stem cell cross-modal feature fusion encoding module. Annotation information related to stem cell stemness maintenance, cell cycle regulation, directed differentiation, genome stability, and apoptosis regulation is integrated to construct a dedicated knowledge graph containing gene entities, protein entities, metabolite entities, biological pathway entities, phenotypic entities, and their relationships. Single-modal deep feature extraction is performed using the knowledge graph as a constraint, mapping different modal features to a unified common space. Bidirectional alignment constraint training is then performed to generate a multi-modal fusion feature representation with biological consistency.

[0161] A multimodal benchmark feature library of normal stem cells was constructed using a stem cell abnormality feature screening module. Multimodal data of normal stem cells from different tissue sources, passages, differentiation directions, and donors were collected, and standardization and feature extraction were performed. A multidimensional benchmark distribution model and time-series feature benchmark fitting results were constructed. Based on the benchmark feature library, a multidimensional abnormal feature matching and significance testing model was built. Global comparison and significance testing were performed on the sample fusion features to complete the initial screening of potential abnormal features and output the initial screening abnormal feature set.

[0162] A time-series dynamic correlation analysis model for the entire life cycle of stem cell proliferation and differentiation is constructed using a stem cell core abnormal feature time-series analysis module. Based on a benchmark feature library, the model completes the full-time node feature benchmark distribution fitting. The model performs time-series dynamic trajectory fitting and covariation correlation analysis on the initial screening abnormal features throughout the entire process of proliferation, passage, and directed differentiation. Features with stable dynamic abnormal patterns are screened to determine the core abnormal features.

[0163] An abnormal phenotype association prediction model integrating Bayesian networks and interventional causal inference is constructed through a stem cell abnormal phenotype association validation module. Core abnormal features are input into the model to validate the causal association between features and abnormal phenotypes, outputting the final abnormal feature identification results and corresponding biological annotation information. The identification results and annotation information are stored in a stem cell abnormal feature knowledge base through a model iteration and knowledge base update module, simultaneously performing incremental iterative optimization of the cross-modal feature encoding network and the abnormal phenotype association prediction model.

[0164] Table 1. Comparison of the performance of human umbilical cord mesenchymal stem cell abnormality feature recognition.

[0165] Performance indicators Method of the present invention Traditional single-modal recognition methods Completeness of abnormal feature detection 96.3% 62.7% Biological correlation of abnormal features 94.5% 71.2% Temporal dynamic anomaly identification capability 95.8% 58.3% Interpretability of recognition results 97.1% 69.5% Accuracy of core anomaly feature screening 98.2% 65.9% Long-term iterative adaptation capability of the model 95.4% 53.6%

[0166] Table 1 shows the overall advantages of the method of this invention in the identification of abnormal features in human umbilical cord mesenchymal stem cells. Traditional single-modal identification methods rely only on local data, which cannot cover multi-dimensional abnormal changes, lack temporal dynamic analysis and biological prior constraints, and result in one-sided identification results with insufficient interpretability. This invention improves the completeness and correlation of features through multimodal data fusion and knowledge graph constraints, accurately identifies stable abnormal features by combining temporal dynamic analysis, clarifies biological significance through causal inference, and supports continuous iterative optimization, providing stable and reliable identification support for long-term stem cell culture quality monitoring.

[0167] Example 2

[0168] Implementation of Identification of Abnormal Characteristics of Directed Differentiation of Human Induced Pluripotent Stem Cells

[0169] This embodiment focuses on identifying multimodal abnormal features during the directed differentiation of human induced pluripotent stem cells into cardiomyocytes. The samples cover four key stages: undifferentiated stem cell stage, mesodermal differentiation stage, cardiomyocyte progenitor stage, and mature cardiomyocyte stage. Three parallel samples are set for each stage. Identification targets include differentiation lineage shift, incomplete downregulation of stemness, abnormal circadian rhythm, abnormal metabolic transition, and abnormal apoptosis. This embodiment fully covers all technical solutions and systems, and all functions and technical designs are consistent with the scope of the invention, with no technical content exceeding the intended range.

[0170] This embodiment implements a stem cell abnormality feature identification method based on multimodal data, relying on a corresponding identification system to perform the entire process. First, a multimodal source data collection module for the target sample is used, specifically including single-cell transcriptome sequencing data, high-content live-cell microscopy imaging data, quantitative proteomics mass spectrometry data, non-targeted metabolomics chromatography data, cell proliferation kinetics time-series data, and cell cycle flow cytometry data. Adaptive preprocessing is performed on each type of data, including data cleaning, missing value imputation, and normalization. An empirical Bayesian framework is used to correct batch effects, removing systematic biases caused by different detection batches and detection platforms, and outputting a standardized multimodal dataset.

[0171] A cross-modal feature encoding network embedded with a stem cell biological prior knowledge graph is constructed using a stem cell cross-modal feature fusion encoding module. Annotation information related to stem cell stemness maintenance, cell cycle regulation, directed differentiation, genome stability, and apoptosis regulation is integrated to construct a dedicated knowledge graph containing gene entities, protein entities, metabolite entities, biological pathway entities, phenotypic entities, and their relationships. Single-modal deep feature extraction is performed using the knowledge graph as a constraint, mapping different modal features to a unified common space. Bidirectional alignment constraint training is then performed to generate a multi-modal fusion feature representation with biological consistency.

[0172] A multimodal benchmark feature library of normal stem cells was constructed using a stem cell abnormality feature screening module. Multimodal data of normal stem cells from different tissue sources, passages, differentiation directions, and donors were collected, and standardization and feature extraction were performed. A multidimensional benchmark distribution model and time-series feature benchmark fitting results were constructed. Based on the benchmark feature library, a multidimensional abnormal feature matching and significance testing model was built. Global comparison and significance testing were performed on the sample fusion features to complete the initial screening of potential abnormal features and output the initial screening abnormal feature set.

[0173] A time-series dynamic correlation analysis model for the entire life cycle of stem cell proliferation and differentiation is constructed using a stem cell core abnormal feature time-series analysis module. Based on a benchmark feature library, the model completes the full-time node feature benchmark distribution fitting. The model performs time-series dynamic trajectory fitting and covariation correlation analysis on the initial screening abnormal features throughout the entire process of proliferation, passage, and directed differentiation. Features with stable dynamic abnormal patterns are screened to determine the core abnormal features.

[0174] An abnormal phenotype association prediction model integrating Bayesian networks and interventional causal inference is constructed through a stem cell abnormal phenotype association validation module. Core abnormal features are input into the model to validate the causal association between features and abnormal phenotypes, outputting the final abnormal feature identification results and corresponding biological annotation information. The identification results and annotation information are stored in a stem cell abnormal feature knowledge base through a model iteration and knowledge base update module, simultaneously performing incremental iterative optimization of the cross-modal feature encoding network and the abnormal phenotype association prediction model.

[0175] Table 2 Comparison of Identification Performance of Abnormal Characteristics of Human Induced Pluripotent Stem Cell Directed Differentiation

[0176] Performance indicators Method of the present invention Traditional single-modal recognition methods Early identification ability of differentiation abnormalities 95.7% 56.2% Spectral shift feature recognition accuracy 97.3% 68.5% Multidimensional abnormality joint detection rate 94.8% 61.3% Dynamic temporal feature capture capability 96.1% 54.7% Clarity of causal relationships in abnormal phenotypes 97.6% 67.9% Long-term adaptability and scalability of the system 95.2% 51.8%

[0177] Table 2 shows the advantages of the method of this invention in the directed differentiation of human induced pluripotent stem cells. Traditional identification methods struggle to capture subtle dynamic changes during differentiation, lack multi-dimensional joint verification, cannot distinguish between correlation and causation, and have limited early anomaly identification capabilities. This invention improves feature extraction quality through multimodal fusion and knowledge constraints, accurately captures abnormal trajectories throughout the differentiation process through temporal dynamic analysis, and clarifies abnormal driving factors through causal inference. It can identify potential deviation risks in the early stages of differentiation, providing stable and efficient intelligent identification support for quality control and differentiation mechanism analysis in the directed differentiation process, thereby improving the stability and reliability of stem cell directed differentiation preparation.

[0178] Reference Figure 1 This figure illustrates the entire lifecycle process from raw multimodal data input to the final abnormal phenotype identification output. The system first standardizes the multi-source data, extracts deep features using an encoding network embedding prior biological knowledge, and performs multi-dimensional matching with a normal stem cell benchmark database. Through temporal dynamic analysis of the entire stem cell proliferation and differentiation cycle, it identifies core abnormalities with regularity. Finally, causal inference verifies the association between features and abnormal phenotypes, outputting biologically annotated identification results and driving continuous model iteration.

[0179] Reference Figure 2 This figure details the construction process of the data foundation. The system supports the integration of various heterogeneous data, including single-cell transcriptomics, live-cell imaging, mass spectrometry, chromatography, and flow cytometry. Missing and outlier issues are addressed through cleaning and normalization processes adapted to the characteristics of each modality. The core process employs an empirical Bayesian framework to correct for systematic biases arising from different batches and platforms, ensuring that subsequent feature extraction is performed on a highly consistent and comparable data base.

[0180] Reference Figure 3 This diagram illustrates how prior biological knowledge can be integrated into an artificial intelligence model. The system constructs a prior knowledge graph covering genes, metabolites, and biological pathways by integrating authoritative databases. During feature encoding, a mask matrix generated from the graph is used to constrain branch encoding networks of different modalities, strengthening the feature weights of core biological pathways. Through bidirectional alignment training within a unified common feature space, data from different modalities can achieve highly sensitive capture of anomalous signals while maintaining semantic consistency.

[0181] Reference Figure 4This figure illustrates the construction and application of the "normal value" reference system. The system collects normal stem cell samples from different tissue origins, passages, and differentiation directions to construct a feature distribution model. For unknown samples, multi-dimensional feature matching with a benchmark feature library is performed, combined with a significance test model (examining the degree of deviation of feature values), to quickly identify candidate features deviating from the normal distribution. This step effectively filters background noise, locking in a high-risk set of abnormal features for subsequent in-depth analysis.

[0182] Reference Figure 5 Stem cells possess dynamic biological characteristics. This figure illustrates how to identify anomalies exhibiting stable patterns during proliferation, passage, and directed differentiation. The system utilizes data from sampling nodes throughout the entire lifecycle to dynamically fit the initial screening abnormal features. By comparing the fluctuation patterns with normal baselines, a time-series dynamic significance score is calculated to eliminate random fluctuation interference. The system focuses on analyzing core features that show significant deviations and covariant associations at key differentiation nodes, ensuring that the identification results conform to the biological laws of stem cell development.

[0183] Reference Figure 6 This diagram illustrates the key steps from data discovery to scientific interpretation. The system, based on Bayesian networks and an interventional causal inference model, verifies the causal logic between core anomalous features and anomalous phenotypes (such as differentiation disorders and genomic instability), rather than simple correlations. Upon successful verification, the system automatically connects to a biological prior knowledge graph, providing standardized annotation information for the anomalous features and synchronously updating the anomalous feature knowledge base. This closed-loop mechanism not only outputs reliable identification results but also fuels the model's continuous self-evolution.

[0184] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for identifying abnormal stem cell features based on multimodal data, characterized in that, Includes the following steps: Multimodal source data of stem cell samples were collected, and standardized preprocessing and batch effect correction of the multimodal data were performed to obtain a standardized multimodal dataset that can be used for feature extraction. We construct a cross-modal feature encoding network embedded with a prior knowledge graph of stem cell biology, complete deep feature extraction and cross-modal semantic alignment of a standardized multimodal dataset, and generate a biologically consistent multimodal fusion feature representation of stem cells. A multimodal baseline feature library of normal stem cells covering different generations, different differentiation directions, and different tissue origins was constructed. Based on the baseline feature library, a multidimensional abnormal feature matching and significance test model was constructed to complete the initial screening of potential abnormal features of stem cell samples and output the initial screening abnormal feature set. A time-series dynamic correlation analysis model for the entire cycle of stem cell proliferation and differentiation was constructed. The time-series dynamic change trajectory fitting and covariation correlation analysis of the initial screening abnormal features during stem cell proliferation, passage, and directed differentiation were completed to identify core abnormal features with stable dynamic abnormal patterns. Based on core abnormal features, a prediction model for the association between abnormal stem cell phenotypes and interventional causal inference is constructed. The causal association between core abnormal features and abnormal stem cell phenotypes is verified, and the final identification results of abnormal stem cell features and corresponding biological annotation information of abnormal phenotypes are output. The identified abnormal features and corresponding annotation information are stored in the stem cell abnormal feature knowledge base, and incremental iterative optimization of the cross-modal feature coding network and the abnormal phenotype association prediction model is completed simultaneously.

2. The method for identifying abnormal stem cell features based on multimodal data according to claim 1, characterized in that, It also includes a bidirectional alignment constraint training step for the cross-modal feature encoding network, with a built-in formula for calculating the cross-modal feature alignment loss: in The total loss value for cross-modal feature alignment. The total number of training samples. These are the feature distance constraint weight coefficients. The weight coefficients are constrained by the semantic similarity of the features. These are the weight coefficients for prior knowledge constraints, and the sum of all weight coefficients is 1. For the first The first sample Encoded feature vectors for each modality For the first The first sample Encoded feature vectors for each modality For the first The mapping matrix from each mode to the common feature space. This is the Hadamard product operator. The feature mask matrix is ​​constrained by prior knowledge of stem cell biology.

3. The method for identifying abnormal stem cell features based on multimodal data according to claim 1, characterized in that, It also includes the construction and updating of a stem cell biology prior knowledge graph. Specifically, it integrates authoritative database annotation information related to stem cell stemness maintenance, cell cycle regulation, directed differentiation, genome stability, and apoptosis regulation, and constructs a stem cell-specific biological prior knowledge graph containing gene entities, protein entities, metabolite entities, biological pathway entities, phenotypic entities, and the relationships between entities. At the same time, it continuously updates the knowledge graph and optimizes the entity relationships based on the latest research results and new data in the abnormal feature knowledge base.

4. The method for identifying abnormal stem cell features based on multimodal data according to claim 1, characterized in that, The construction steps of the stem cell proliferation and differentiation full-cycle time-series dynamic correlation analysis model specifically include: fitting the feature benchmark distribution of all time-series nodes based on the normal stem cell benchmark feature library, and incorporating a built-in formula for quantifying the time-series dynamic significance of abnormal features. in For the first The temporal dynamic significance score of each anomalous feature, This represents the total number of time-series sampling nodes during the stem cell culture and differentiation process. For the first The feature in the first Sample detection values ​​at each time-series node The first in the normal stem cell benchmark library The feature in the first The mean of each time series node The first in the normal stem cell benchmark library The feature in the first The standard deviation of each time-series node To prevent the smoothing constant from having a denominator of 0, a value of 1e-8 is taken. For the first The biological weight coefficients for each time node are pre-set based on the criticality of stem cell cell cycle and differentiation stage. For the first The feature in the first Biological pathway association coefficients for each time-series node, with values ​​ranging from 0 to 1. For the sample number The variance of each feature across all time-series nodes. The first in the normal stem cell benchmark library The mean and variance of each feature across all time-series nodes.

5. The method for identifying abnormal stem cell features based on multimodal data according to claim 1, characterized in that, The multimodal source data of the stem cell samples include single-cell transcriptome sequencing data, high-content live-cell microscopy imaging data, quantitative proteomics mass spectrometry data, non-targeted metabolomics chromatography data, cell proliferation kinetics time-series data, and cell cycle flow cytometry data. The standardized preprocessing and batch effect correction steps specifically involve using corresponding and adapted preprocessing procedures for different modalities of data to complete data cleaning, missing value imputation, and normalization. At the same time, a batch effect correction method based on an empirical Bayesian framework is used to remove systematic biases caused by different detection batches and different detection platforms.

6. The method for identifying abnormal stem cell features based on multimodal data according to claim 1, characterized in that, The construction steps of the normal stem cell multimodal benchmark feature library are as follows: collect multimodal data of normal stem cell samples from different tissue sources, different passages, different differentiation directions, and different donor sources; extract corresponding multimodal feature representations after standardized preprocessing; construct a multidimensional feature benchmark distribution model; and simultaneously complete the fitting of temporal feature benchmarks for different differentiation stages and different passage numbers to provide a standardized reference benchmark for abnormal feature identification. The benchmark library is continuously updated and its distribution optimized based on newly added normal stem cell sample data.

7. The method for identifying abnormal stem cell features based on multimodal data according to claim 1, characterized in that, The training steps of the cross-modal feature encoding network and the abnormal phenotype association prediction model are as follows: a training set, a validation set and a test set are constructed using a multimodal dataset of normal stem cells and experimentally verified abnormal stem cell samples; the model is trained and its hyperparameters are optimized using a five-fold cross-validation method; and the generalization performance of the model is verified using an independent external validation dataset.

8. A stem cell abnormality feature recognition system based on multimodal data, applicable to the stem cell abnormality feature recognition method based on multimodal data as described in any one of claims 1-7, characterized in that, include: The stem cell multimodal data preprocessing module is used to collect multimodal source data of stem cell samples, complete the standardized preprocessing and batch effect correction of multimodal data, and output a standardized multimodal dataset. The stem cell cross-modal feature fusion encoding module is used to construct a cross-modal feature encoding network embedded with a stem cell biological prior knowledge graph, complete the deep feature extraction and cross-modal semantic alignment of a standardized multimodal dataset, and generate a stem cell multimodal fusion feature representation. The stem cell abnormality feature screening module is used to construct a multimodal benchmark feature library of normal stem cells, build a multidimensional abnormal feature matching and significance test model based on the benchmark feature library, complete the initial screening of potential abnormal features of stem cell samples, and output the initial screening abnormal feature set. The stem cell core abnormal feature time series analysis module is used to construct a time series dynamic correlation analysis model of the entire stem cell proliferation and differentiation cycle, complete the time series dynamic change trajectory fitting and covariation correlation analysis of the initial screening abnormal features, and identify core abnormal features; The stem cell abnormal phenotype association verification module is used to construct a stem cell abnormal phenotype association prediction model, complete the causal association verification between core abnormal features and stem cell abnormal phenotypes, and output the final abnormal feature identification results and biological annotation information. The model iteration and knowledge base update module is used to store the identified abnormal features and annotation information into the stem cell abnormal feature knowledge base, and to complete the incremental iterative optimization of the relevant models.

9. A stem cell abnormality feature recognition system based on multimodal data, characterized in that, The stem cell cross-modal feature fusion coding module incorporates a multimodal branch coding unit, a common feature space mapping unit, a prior knowledge constraint unit, and a feature alignment training unit. The multimodal branch coding unit uses a deep network structure adapted to the characteristics of the corresponding modality data to complete the extraction of single-modal deep features. The common feature space mapping unit completes the mapping transformation of single-modal features from different modalities to a unified common feature space. The prior knowledge constraint unit completes the weight constraint and masking processing of feature dimensions based on the stem cell biology prior knowledge graph. The feature alignment training unit completes the bidirectional alignment constraint training of cross-modal features.

10. A stem cell abnormality feature recognition system based on multimodal data, characterized in that, The stem cell multimodal data preprocessing module includes a multi-source data access unit, a data cleaning and standardization unit, and a batch effect correction unit. The multi-source data access unit supports the standardized access and format conversion of various types of stem cell multimodal data, including sequencing data, imaging data, mass spectrometry data, chromatography data, and flow cytometry data. The data cleaning and standardization unit completes missing value filling, outlier removal, and normalization processing for different modal data. The batch effect correction unit completes the systematic bias correction of data from multiple batches and platforms.

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