120 results about "Subtype classification" patented technology

A juvenile depression cognitive impairment subtype classification and prognosis prediction method relates to the technical field of medical treatment, and mainly comprises the following steps: performing clinical evaluation and therapeutic response evaluation on a subject, performing MRI and magnetoencephalogram data acquisition, constructing a whole brain MSN of the subject, identifying MSN abnormal characteristics, obtaining functional connection change of a frequency band when magnetoencephalogram is abnormal, and determining the cognitive impairment subtype classification and prognosis prediction of the cognitive impairment subtype of the subject. A subtype classification model is established by fusing the MSN and cognitive function evaluation data, and a prognosis prediction model is established by analyzing MSN abnormal features, functional connection changes of frequency bands during abnormality, multi-dimensional treatment reactions and high-risk behaviors. According to the method, different levels of fusion measurement are carried out on the juvenile depression with cognitive function impairment brain mechanism through multi-modal brain images, a subtype classification model with diagnosis and treatment values is established, and a prognosis prediction model with clinical transformation potential is constructed; therefore, a theoretical basis and a technical means are provided for individualized precise diagnosis and treatment of the cognitive impairment of the juvenile depression.

The invention relates to the technical field of pathological image analysis and mining, and particularly discloses an ovarian cancer subtype classification method based on prototype learning and multi-view deep embedding clustering, and the method comprises the following steps: S1, collecting a tissue pathological image of an ovarian cancer patient and a corresponding full-view digital pathological image; and S2, generating a multi-view data set. According to the ovarian cancer subtype classification method based on prototype learning and multi-view deep embedding clustering, the problem that in the prior art, patch-level labels are generally lacked in the field of multi-instance pathological images, so that many natural image processing methods cannot be applied to the field of pathological images is solved. A ResNet backbone network is used for extracting features of pathological images under the maximum magnification, a small number of pathology prototypes are introduced to guide deep embedded clustering through pathology expert priori knowledge, a pathology image spectrogram is introduced to serve as a reference view, and the accuracy and stability of clustering are enhanced.

The invention discloses a data processing method and system for tumor cell detection, and relates to the technical field of data processing.The method comprises the steps that cells are sequenced, and cell metabolism is monitored; performing time delay reconstruction according to the embedding dimension and the delay time, mapping gene expression data to a reconstruction phase space, and calculating a chaos degree of gene expression; establishing a biomarker white list, inputting the chaos degree into an adaptive weight distributor, setting feature retention weights for markers in the white list, constructing a protective auto-encoder, dynamically adjusting the dimension of a bottleneck layer, and performing forced constraint when the markers in the white list are detected; the method comprises the following steps: extracting coupling features through metabolic oscillation data and physical microenvironment data, calculating gradient sensitivity of shear stress to metabolic oscillation, carrying out feature fusion on the coupling features and gene expression features, and constructing a subtype classification boundary for the fused features through feature vector selection in combination with the gradient sensitivity. Accurate dimension reduction of data and deep analysis of biological characteristics are realized.

The invention provides a breast cancer molecular subtype recognition and prediction method and system based on an ultrasonic image, and the method comprises the steps: carrying out the feature fusion preprocessing of a breast ultrasonic image based on acoustic impedance difference, and obtaining a standardized ultrasonic image; dividing the image into fan-shaped regions based on the radial fiber structure of the mammary gland, and dynamically adjusting the window size according to tissue characteristics to carry out adaptive window segmentation to obtain a window set carrying anatomical structure position information; inputting the window set into a pre-trained cross window Transform model for feature extraction, and analyzing a feature association mode between the windows through a cross window attention mechanism to obtain a multi-dimensional ultrasonic feature vector; and inputting the feature vector into a classifier for molecular subtype classification prediction to obtain a breast cancer molecular subtype identification prediction result. According to the method, through adaptive window segmentation, breast anatomical structure information is utilized, local and global feature association is captured through a cross window attention mechanism, and the prediction accuracy is improved.

Provided herein is a consensus molecular subtype (CMS) classifier for colorectal cancer patients. Also provided are methods of using the classifier to identify a clinically beneficial therapeutic regime for each patient as well as methods of treating a patient accordingly Custom Nanostring code sets, which work on formalin-fixed, paraffin-embedded samples, are provide for use in determining the CMS for a colorectal cancer patient.

The invention relates to the technical field of bioinformatics, in particular to a method for typing acute respiratory distress syndrome biological subtypes. The method comprises the following steps: a) acquiring multi-omics data and carrying out standardized preprocessing; the multi-omics data comprises transcriptomics data, proteomics data and metabonomics data of a biological sample source; b) constructing a similarity network of each group by using a similarity fusion network (SNF), and obtaining a uniform sample similarity matrix through multi-group network fusion and iteration; multiple collaborative principal component analysis (MCIA) is adopted to carry out dimension reduction on multi-omics data so as to realize visualization of a clustering result; carrying out multi-omics joint discrimination modeling under the guidance of SNF clustering by using a data integration analysis (DIABLO) method so as to identify key feature variables; and carrying out biological subtype classification based on the clustering result of the steps.

The invention discloses a depression recurrence risk intelligent early warning method, system and device based on multi-modal data and artificial intelligence, and relates to the field of depression classification early warning, and the method comprises the steps: carrying out the individualized deviation calculation based on the baseline features and multi-modal brain image features of a target patient, obtaining an individual multi-modal brain image deviation feature vector; dimension reduction processing is carried out on the individualized multi-mode brain image deviation feature vector, the genetic features and the environment and clinical features, the individualized multi-mode brain image deviation feature vector, the genetic features and the environment and clinical features are input into a pre-trained layered integrated classification model and a pre-trained layered integrated risk early warning model, and depression subtype classification tags and risk probabilities are obtained; in the pre-training process of the hierarchical integration classification model and the hierarchical integration risk early warning model, multi-modal feature fusion and hierarchical integration learning strategies are adopted, and samples from a plurality of data centers are used for model training and verification. According to the method, the accuracy and individualization degree of classification and early warning are improved, and the generalization ability and robustness of the model are also improved.

The invention belongs to the technical field of histopathological image auxiliary diagnosis, and particularly relates to a breast cancer molecular typing method and system based on a histopathological image, and the method comprises the steps: obtaining a digital pathological image, carrying out the feature extraction of the digital pathological image, and obtaining the feature embedded representation of the digital pathological image; and inputting the feature embedded representation into a deep learning-based typing model to obtain the probability of each molecular subtype. In a deep learning-based typing model, a long-range dependency relationship is captured through an attention mechanism, a local space structure is extracted by a convolutional neural network module, and effective global and local fusion is further realized through an interaction mechanism. According to the method, the dynamic weighting of the attention mechanism is combined with the residual learning of the convolutional neural network module, so that the modeling capability of the model for a complex tissue mode is effectively improved, and the method has relatively high efficiency in processing high-dimensional spatial heterogeneity data, thereby providing relatively high efficiency and accuracy in triple negative breast cancer molecular subtype classification.

The invention relates to the technical field of image data processing, in particular to an immune cell image data classification and recognition system which comprises a space-time registration module, a dynamic trajectory analysis module, a membrane table deformation detection module and a subtype classification decision module. According to the method, coordinate system alignment is achieved through affine transformation, registration precision is improved in combination with a gray variance threshold screening mechanism, a three-frame sliding window calculates a gray gravity center displacement vector, cosine similarity verifies directional convergence, track continuity capture capability is enhanced, noise interference pseudo tracks are filtered, and the method is suitable for large-scale popularization and application. A curvature-area coupling model quantifies membrane boundary gradient resistance, three-point extreme value detection locates a deformation area, edge detection geometric characterization limitation is broken through, a support vector machine analyzes a track consistency coefficient, dynamic threshold segmentation quantifies resistance mutation, a multi-dimensional nonlinear discrimination boundary is constructed, subtype discrimination specificity and sensitivity are improved, and the method has the advantages of being high in accuracy and high in reliability. And performing spatio-temporal registration, track verification, mechanical analysis and feature fusion progressive processing to realize behavior feature full-dimensional analysis.

The invention relates to the field of disease diagnosis, and particularly discloses application of Ki-67 protein, CHG protein and CTGF protein or detection substances thereof in preparation of products for tumor subtype classification, and the tumor is olfactory neuroblastoma. Representative molecular markers which correspond to various NBM types, can be verified by IHC staining and are limited in number are screened out from an ONB NBM trityping gene set obtained by an scRNA sequencing technology, and a simple optimization scheme which aims at the ONB NBM trityping and is suitable for clinical examination and classification is established through FFPE clinical queue verification.

The invention provides a disease marker subtype classification determination method, and belongs to the technical field of disease marker subtype classification, and the method comprises the following steps: firstly, extracting protein from a patient sample, separating through a dimensional electrophoresis technology, and obtaining a protein expression matrix through Coomassie brilliant blue dyeing and an image analysis system; differentially expressed proteins are determined, and sequence information is obtained through mass spectrum identification. And extracting network node features by using a graph neural network method, and carrying out function annotation analysis and path identification. Patient samples are grouped by using a multi-level clustering method, and feature extraction and optimization are realized by training an embedded neural network model. And inputting the extracted features into a pre-trained function enrichment analysis model, and determining the molecular pathway features of the disease subtype. And finally, the reliability of a classification result is confirmed through an immunoblotting technology and independent patient queue verification, subtype classification of the disease markers is completed, and the problem that in the prior art, the recognition degree of subtype classification of the disease markers is not high enough is solved.

The invention discloses an intelligent diagnosis and typing method for pneumonia-related ARDS, and relates to the technical field of intelligent diagnosis and typing methods for ARDS, and the method comprises the steps: employing a data preprocessing method to carry out cleaning and standardization processing on pneumonia patient clinical data from MIMIC-IV and eICU databases, and obtaining a standardized data set; a correlation weight algorithm is adopted to screen 52 candidate variables in the standardized data set, and 18 key prediction factors are obtained; carrying out model training on the screened key predictive factors by adopting a machine learning stacking method to obtain a pneumonia-related ARDS diagnosis model; performing clustering analysis on the clinical data of the pneumonia-related ARDS patients by adopting a k-means clustering method, and dividing the patients into three clinical phenotypes based on nine input factors to obtain a subtype classification result; verifying a subtype classification result by adopting a random forest method to obtain a clinical subtype prediction model with high accuracy; and integrating and deploying the diagnosis model and the subtype classification model by adopting a webpage application program.

The invention discloses a depression subtype classification system based on a cross-species multi-mode brain image. The method comprises the following steps: firstly, obtaining cross-species multi-mode brain image data and depression brain image data and carrying out preprocessing; constructing a multi-modal mapping process between the human brain and the chimpanzee brain to obtain a homologous brain map of the human brain and the chimpanzee brain; the method comprises the following steps: constructing connection characteristics of a human brain and a chimpanzee brain by using whole-brain-based probability fiber tracking, and extracting specific connection characteristics of the human brain through cross-species comparison; constructing a depression typing model based on the specific connection characteristics of the human beings; and verifying the typing result based on the functional image data index and the brain function connection. According to the invention, from the perspective of species evolution, specific connection characteristics of human beings are constructed through cross-species comparison, depression subtype typing is realized, and a new perspective and a new method are provided for understanding depression subtypes and biological basis thereof.

The invention discloses a detection method based on combination of anti-NF155 antibody detection and enzyme-linked immunosorbent assay. According to the detection method, an analysis model based on an XGBoost model algorithm is established by adopting anti-NF155 antibody detection of a patient and detection data of an enzyme-linked immunosorbent assay in combination with autologous clinical data of the patient, so that CIDP subtype classification is performed on the patient, and personalized treatment scheme suggestions are provided in combination with illness state dynamic data of the patient. Meanwhile, the invention provides a corresponding detection system which comprises a laboratory, a data storage library and an analysis unit, the anti-NF155 antibody detection is executed through laboratory equipment, and detection data and patient autologous clinical data are stored in the data storage library. The analysis unit establishes an analysis model based on an XGBoost model algorithm based on a machine learning algorithm.

The invention discloses an early prediction model construction method for senile sarcopenia and metabolic high-risk phenotypes. The method comprises the steps that 3D scanning data, body composition data and health phenotype data of senile patients are acquired, and data preprocessing is carried out; respectively extracting feature vectors of the three types of data after data preprocessing, and performing alignment and weighted fusion to obtain fusion vectors; inputting the fusion vector into a subtype classification model for training to obtain a preliminary prediction model; in the training process of the subtype classification model, an unsupervised learning method and a supervised learning method are adopted at the same time, and a staged freezing strategy and a five-fold cross validation method are adopted; the preliminary prediction model is verified and finely adjusted by using a verification data set prepared in advance, so that a final early prediction model of senile sarcopenia and metabolic high-risk phenotypes is obtained, and accurate diagnosis of sarcopenia and intelligent identification of metabolic high-risk subtypes can be realized through the constructed prediction model.

The invention discloses a cancer profiling classification and prognosis prediction method based on generic cancer multi-omics data, and relates to a cancer profiling classification and prognosis prediction method. The invention aims to solve the problems of cancer subtype classification and prognosis prediction in generic cancer multi-omics. The method comprises the following steps: step 1, acquiring a multi-omics generic cancer data set, wherein the multi-omics generic cancer data set comprises a data set consisting of mRNA (messenger ribonucleic acid), DNA (deoxyribonucleic acid) methylation, miRNA (micro Ribonucleic Acid) and clinical prognosis data; 2, preprocessing the multi-omics cancer data set in the step 1; step 3, constructing a deep neural network model; 4, training the deep neural network model constructed in the step 3 based on the multi-omics generic cancer data set preprocessed in the step 2; and 5, carrying out cancer subtype classification and prognosis prediction on to-be-detected data based on the deep neural network model trained in the step 4. The invention belongs to the technical field of bioinformatics.

Described herein are gene features that provide prognosis, diagnosis, treatment and molecular subtype classification of cancer by genomic and epigenomic profiling, including immune checkpoint regulators such as Programmed Death Ligand 1 (PDL-1). Using the methods and compositions described herein, specific and sensitive detection of biomarkers of interest is provided. Such biomarkers indicate disease pathogenesis, which provides opportunities for selection of treatments, including treatment regimens intended to overcome tolerance mechanisms.

The invention provides a cancer subtype classification method, system, equipment and program product based on ferroptosis related miRNA, and belongs to the field of intelligent medical treatment. The breast cancer is divided into four subtypes on the basis of ferroptosis related miRNA, the breast cancer FAP + subtype is identified on the basis of ferroptosis activity, the molecular characteristics of the FAP + subtype of an individual breast cancer patient are measured by establishing FAPscore, and the higher the FAPscore is, the more remarkable the FAP + subtype characteristics of the breast cancer patient are. The invention also finds that FAPscore can be used for predicting response, prognosis and drug sensitivity of individual cancer patients to immunotherapy response, and the discovery has wide cross-cancer species applicability and is not limited to breast cancer. The invention provides a new insight for a more effective and individualized treatment strategy of cancer.

The application discloses a dynamic prototype multi-model medical image classification method based on feature credibility evaluation, and relates to the technical field of medical image processing.The method effectively solves the problems in traditional medical image subtype classification, such as lack of high-quality labeled data, high uncertainty of pseudo-labels, and difficulty of static prototypes in adapting to dynamic changes of lesions, and the like.Through construction of a multi-structure feature extraction network and completion of hierarchical feature fusion, the method combines feature cross learning, local attention modeling and expanded convolution to supplement context information, and strengthens semantic consistency and structural continuity of lesion region features.Meanwhile, the method constructs a spatial similarity graph through cosine similarity, and models and fuses a spatial uncertainty graph with any and cognitive uncertainty based on a Dirichlet distribution, obtains a reliable evidence graph through exponential fusion, and generates a pseudo-label with sample-level confidence, so that effective supervision information is accurately screened from a feature level, cumulative deviation of false pseudo-labels is greatly reduced, and stability of a semi-supervised learning process is improved.

The invention discloses a disease subtype classification method, a disease subtype classification device, electronic equipment and a computer program product. The method comprises the following steps: acquiring at least two types of omics data of a plurality of patient samples with a target disease; respectively constructing a similarity matrix under each type of omics data, wherein the similarity matrix is used for describing the feature similarity of any two patient samples under the corresponding omics data; fusing the similarity matrixes under various omics data to obtain a fused similarity matrix; based on the fusion similarity matrix, multi-stage clustering analysis is carried out, a typing result for the target disease is obtained, and the multi-stage clustering analysis comprises at least two of the following clustering processing modes: spectral clustering processing, unsupervised clustering processing and consensus clustering processing. The scheme of the invention can help to improve the stability and resolution of typing.

This platform is a privacy-preserving, federated learning system designed to integrate whole-slide digital pathology images with matched molecular profiling and clinical metadata for improved diagnosis, subtyping, and prognostic estimation of gynecologic tumors. The architecture comprises local institutional nodes that retain raw patient data while participating in distributed model training coordinated by a central orchestration server. Each local node preprocesses whole-slide images into patch-level tensors, extracts visual embeddings via convolutional backbones, and processes molecular vectors (e.g., somatic mutations, expression summaries, copy-number measures) via a molecular encoder. A multimodal fusion module - implemented as an attention- based transformer - integrates image and molecular embeddings into a unified representation used by multi-task heads for classification (histologic subtype, diagnostic label) and regression (risk score). The federated learning controller aggregates encrypted model updates (FedAvg) and returns improved global weights without exchanging raw data, enabling cross-site generalization while preserving patient privacy. Explainability components generate attention maps and tile-level saliency (Grad-CAM style) linked to molecular features, providing interpretable morpho- molecular correlations to pathologists. The platform supports API integration with PACS / LIMS, conforms to privacy standards via optional differential privacy and secure aggregation layers, and is extensible to additional omics modalities or transfer / fine-tuning workflows for related tumor types. By combining multimodal fusion, federated training, and clinician-facing interpretability, the system accelerates robust, generalizable AI for precision pathology in gynecologic oncology.

The application relates to the field of biomedical technology, and discloses a diagnostic agent for identifying melanoma molecular subtype typing and application, wherein the diagnostic agent comprises a first antibody specifically combined with a SOX10 protein and a second antibody specifically combined with an EGR1 protein; the diagnostic agent can be applied to preparation of a diagnostic product for evaluating the prognostic effect of a melanoma patient, preparation of a diagnostic product for predicting the treatment sensitivity of melanoma to a BRAF inhibitor, and preparation of a diagnostic product for guiding an individualized treatment scheme of melanoma. The diagnostic agent for melanoma molecular subtype typing can be directly transformed into clinical practice, and can assist in realizing real individualized treatment.

The invention relates to the technical field of medical image processing, and discloses a chronic obstructive pulmonary emphysema distribution quantitative method and system based on multi-scale segmentation, and the method comprises the steps: carrying out the anisotropic diffusion filtering noise reduction of a chest CT image; segmenting a lung field and removing a blood vessel bronchial structure by adopting a region growing algorithm; multi-scale image representation is constructed based on a Gaussian pyramid, an emphysema candidate area is identified in a coarse scale layer, and a boundary is accurately drawn by adopting a self-adaptive threshold value in a fine scale layer; extracting local texture features to distinguish the lobular central emphysema and the total lobular emphysema; dividing severity levels according to spatial aggregation characteristics and density gradient distribution, and calculating an air swelling volume ratio and a distribution heterogeneity index; the three-dimensional pseudo-color volume is used for drawing visualization, a structured quantitative report is generated, accurate segmentation and subtype classification of the emphysema area are achieved, and comprehensive quantitative analysis indexes are provided.

To provide a method for identifying a therapy recommendation for a subject exhibiting dysregulated host response.SOLUTION: A classification of the subject of subtype A, subtype B, or subtype C is obtained. The therapy recommendation for the subject is identified based at least in part on the classification. Responsive to the classification of the subject comprising the subtype A, the therapy recommendation can be no immunosuppressive therapy. Responsive to the classification of the subject comprising the subtype B, the therapy recommendation can be no therapy recommendation, immune stimulation therapy, suppression of immune regulation therapy, blocking of immune suppression therapy, blocking of complement activity therapy, and / or anti-inflammatory therapy. Responsive to the classification of the subject comprising the subtype C, the therapy recommendation can be no therapy recommendation, immune stimulation therapy, suppression of immune regulation therapy, blocking of immune suppression therapy, modulators of coagulation therapy, and / or modulators of vascular permeability therapy.SELECTED DRAWING: Figure 1A

The present application relates to a kind of melancholic depression plasma biomarker kit detection method, for detecting the plasma biomarker of melancholic depression.The present application provides a kind of application method by detecting plasma adiponectin (Adiponectin) protein as the application method of melancholic depression biomarker, the present application method is used for the subtype classification diagnosis of melancholic depression for non-diagnostic purposes, improve the sensitivity and specificity of melancholic depression screening.

The application provides a training method of a cancer histological subtype classification model and a storage medium. The method comprises the following steps: identifying a tumor image region in a HE-stained slice image, and dividing the tumor image region into a plurality of non-overlapping sub-slice images; inputting each sub-slice image corresponding to each HE-stained slice image in a sample set into a feature extraction network to obtain a basic feature vector of the sub-slice image; inputting the basic feature vectors of all sub-slice images of the HE-stained slice image into a connected attention network and a hybrid density network to construct an enhanced feature vector of the HE-stained slice image; inputting the enhanced feature vector of the HE-stained slice image into a classifier to output a cancer histological subtype prediction result of the HE-stained slice image; and adjusting hyperparameters to generate a trained cancer histological subtype classification model.

The invention discloses a full-slice image cancer prediction and subtype classification method, system and device, and relates to the technical field of image processing and medical artificial intelligence. Comprising the following steps: preprocessing a full-slice image, cutting the full-slice image into image blocks with position coordinates, and extracting features; reconstructing the feature sequence into a two-dimensional feature map which retains the original spatial topology through a spatial recovery module; scanning and fusing along eight directions including a horizontal direction, a vertical direction and a plurality of diagonal lines by using a hyper-cross scanning module so as to capture multi-direction local space correlation; multi-scale global features are extracted and fused by adopting convolution layers with different expansion rates through a pyramid module; and finally, outputting a prediction result and a subtype label through a customized classifier, and generating a focus attention heat map. Through the architecture of spatial reconstruction-multidirectional scanning-multi-scale fusion, while the linear calculation complexity of O (n) is kept, the small focus recognition capability and classification precision are remarkably improved, and an efficient and reliable technical scheme is provided for digital pathological diagnosis.

The invention relates to an AD early intervention-oriented multi-modal image omics feature and multi-strategy algorithm collaborative MCI subtype classification system. The system comprises a graph acquisition unit, a multi-modal feature extraction unit, a multi-strategy feature selection unit, a hybrid machine learning classification unit and a model integration and evaluation unit. According to the method, a systematic solution of MCI subtype classification is realized by establishing a complete technical chain from multi-modal feature extraction, multi-strategy feature selection to hybrid algorithm optimization. The system not only overcomes the problem of single feature extraction in traditional research, but also remarkably improves the accuracy and generalization ability of a classification model through multi-strategy feature selection and a hybrid machine learning framework. Especially, a cascading strategy combining traditional machine learning and deep learning is adopted, so that the interpretability of the model is ensured, the feature characterization capability is enhanced, reliable technical support is provided for early diagnosis and intervention of AD, and meanwhile, the stability and clinical applicability of a classification result are improved.

Described herein are gene features for providing prognosis, diagnosis, treatment and molecular subtype classification of cancer by genomic and epigenomic profiling, methods and compositions for determining cancer and subtypes, including breast cancer, and provide specific and sensitive detection of biomarkers of interest. Such biomarkers indicate disease pathogenesis, which provides opportunities to select treatment, including treatment regimens intended to identify responsive candidates and overcome resistance mechanisms.

The invention relates to a cancer subtype classification method, system, device and medium, and belongs to the technical field of cancer subtype classification, the method comprises the following steps: obtaining gene expression, DNA methylation and miRNA omics data of a to-be-trained sample, and respectively constructing respective sample similarity networks; using Chebyshev GCN to carry out feature extraction on a sample similarity network constructed based on each kind of omics data; constructing a first comparative learning loss function and a second comparative learning loss function according to the features extracted by the Chebyshev GCN, and constructing a jointly trained loss function based on the two comparative learning loss functions to train the Chebyshev GCN; using the trained Chebyshev GCN to output fusion features about a to-be-detected sample, and classifying the cancer subtypes of the to-be-detected sample based on the fusion features. According to the method, the features of the cancer subtypes can be effectively extracted, and then effective classification is carried out.