4801results about "Proteomics" patented technology

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

A federated distributed computational system enables secure oncological therapy optimization through multi-expert integration and advanced uncertainty quantification. The system implements a multi-expert integration framework that coordinates domain-specific knowledge through token-space communication for precision oncological treatment, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration. Through a distributed graph architecture, the system enables advanced fluorescence imaging with wavelength-specific targeting, multi-level uncertainty estimation combining epistemic and aleatoric approaches, and multi-scale tensor-based integration with adaptive dimensionality control. The system implements light cone search and planning for adaptive treatment strategy optimization, enabling medical institutions and research organizations to collaborate on complex oncological therapy projects while maintaining strict data privacy controls.

Methods and kits are provided for diagnosing of neuropsychiatric syndromes concurrent with SLE (NPSLE) and for determining whether an SLE subject is at risk of developing a neuropsychiatric disease.

The invention discloses an intestinal cancer metastasis prediction method and device based on spatial omics, a medium and equipment, and the method comprises the steps: collecting original multi-omics data, and carrying out modal alignment and quality control processing to obtain pre-processed multi-omics data comprising second spatial transcriptome data, second single-cell RNA sequencing data and second pathological image data; performing cross-modal semantic embedding on the second spatial transcriptome data based on the second single-cell RNA sequencing data to generate a spatial enhanced expression profile; performing multi-scale graph construction on the second spatial transcriptome data and the second pathological image data, and extracting spatial heterogeneity features; inputting the spatial enhancement expression spectrum and the spatial heterogeneity features into a pre-trained metastasis risk prediction model, and outputting a liver metastasis probability spatial heat map and a key driving feature list; and finally generating a clinical prediction report containing high-risk area positioning. According to the method, through dynamic optimization of spatial resolution and multi-scale feature collaborative modeling, the sensitivity of early transfer detection is remarkably improved.

Platforms, systems, and methods for genetic generalization in synthetic biology development. According to one aspect, there is provided a method for predicting performance associated with genetic edits, the method comprising: receiving, by a platform, information about a strain of a microorganism, wherein the information about the strain comprises information describing a plurality of genetic edits to a base strain of the microorganism; generating, by the platform, a set of genetic embeddings based on the information about the strain, wherein the generating comprises processing the information about the strain using one or more embedding models, wherein each of the one or more embedding models: receives the information about the strain of the microorganism as input; and applies computational transformations to the input using a corresponding embedding model to generate a multi-dimensional vector representation for each of the plurality of genetic edits.

The invention provides a brain tumor survival prediction method and system based on a multi-modal medical knowledge graph, and belongs to the technical field of brain tumor survival prediction. The multi-modal medical knowledge graph based on third-party knowledge base fusion is constructed; performing feature extraction on the brain tumor multi-modal data; searching an entity corresponding to the brain tumor related data in the multi-modal medical knowledge graph, and converting the entity into feature representation by using an entity representation learning method; the learned feature representation related to the brain tumor type complements the missing data mode, and finally the complemented features are input into a pre-trained survival prediction model to achieve brain tumor survival prediction. According to the multi-modal medical knowledge graph, comprehensive medical knowledge support meeting clinical requirements is provided; the multi-modal mapping knowledge domain is used for missing modal completion of brain tumor survival prediction, and a completion feature is generated by querying an associated entity through the mapping knowledge domain, so that the problem of weak modal missing processing capability in the prior art is solved.

The invention belongs to the field of bioinformatics, and relates to an anti-cancer drug response prediction method based on an attention mechanism. The method comprises the following steps: firstly, capturing uniform-dimension drug and cancer cell line characteristics through a multi-layer perceptron; secondly, fusing drug characteristics by adopting a Transform encoder, and constructing a cell encoder for cancer cell line characteristic polymerization; then, designing a cross-modal cross fusion module to promote information interaction between the two; and finally, predicting a semi-suppressed concentration value subjected to logarithmic transformation between the two through a multi-layer perceptron. Experimental results show that compared with an existing optimal method, the method has the advantage that the RMSE is reduced by 2.9%. According to the method, accurate prediction of the anti-cancer drug response is achieved by integrating drug and cancer cell line data, screening of potential anti-cancer drugs can be accelerated, personalized treatment schemes can be optimized, the cure rate of cancer patients is further increased, and the method has great significance in cancer treatment.

The invention relates to the technical field of genome data analysis, and discloses an intelligent system, method and equipment for assisting multi-step genome data analysis, and the system comprises a dialogue agent which is used for generating a corresponding answer according to a question of a user, or reading an analysis plan file generated by a workflow agent, generating an analysis interpretation text for the analysis plan file; the workflow agent is used for generating a structured task execution plan according to the to-be-executed analysis task and executing the to-be-executed analysis task; and the modeling analysis agent is used for generating a configuration file and a script based on the user request, constructing a model and generating an analysis result corresponding to the user request in combination with the workflow agent. Through multi-agent cooperation, task division and cooperative scheduling are realized, each agent independently completes task planning, execution control, model analysis and other functions, the bottleneck problem of processing of a traditional single model in a complex process is avoided, error accumulation is reduced, and the execution efficiency and stability of the whole process are improved.

The invention discloses a protein palmitoyl transferase prediction method and system based on a multi-branch deep convolutional neural network, and belongs to the technical field of bioinformatics and artificial intelligence. The method comprises the following steps: S1, obtaining a to-be-detected protein sequence; s2, inputting the protein sequence into a pre-trained iPalmT model; and S3, judging whether the target protein is palmitoyl transferase or not according to a model output result. The iPalmT model comprises a coding module, two paths of parallel convolution branches, a feature fusion module and a classification module; and after the convolution layers of each convolution branch are stacked, an SE module is arranged and is used for channel weighting and feature re-calibration. The model extracts multi-level sequence features through convolution kernels of different scales, realizes high-precision prediction through feature fusion and a residual structure, can automatically learn multi-scale features from large-scale data, realizes end-to-end palmitoyl transferase recognition, and has high accuracy and good universality.

The invention discloses an mRNA non-coding region sequence optimization method and system and electronic equipment, and the mRNA non-coding region sequence optimization method comprises the steps: constructing an initial candidate library according to a target protein; inputting the initial candidate library into a pre-trained mRNA sequence optimization model to obtain a prediction data set; performing multi-dimensional scoring and sequence optimization on the prediction data set to obtain a sequence recommendation group; performing biological verification on the sequence recommendation group to obtain an optimized mRNA sequence; wherein the prediction data set comprises a sequence ID, a sequence content, a prediction TE score and a confidence interval. According to the method, the translation efficiency of the mRNA sequence can be efficiently and accurately predicted, the candidate sequence with high expression potential is screened out, meanwhile, the consumption of computing resources is reduced, and the overall design cost is reduced.

The present invention relates to a computer-implemented multi-module classifier method and system for providing a pathogenicity classification score of a variant of a protein of interest. The classifier comprises a sequence module based on a protein language model (PLM); a structure module based on a graph neural network (GNN); a property module; and a unified head module based on a machine learning model. The invention further relates to methods for preparing, training, and implementing the multi-module classifier system.

The invention belongs to the technical field of antibacterial peptide activity identification and evaluation, and relates to an antibacterial peptide activity and MIC value combined prediction framework based on cross-modal deep learning, and the framework uses a protein language model ESM2 to respectively carry out token-level semantic embedding coding on an antibacterial peptide sequence and a pathogen protein sequence; performing cross-modal feature extraction and fusion through a multi-branch structure comprising a Mama module, a multi-head self-attention mechanism and DASM 1D convolution; a multi-task decoding structure is adopted to realize antibacterial peptide activity classification and MIC value regression prediction at the same time; according to the method, functional characteristics in the sequence can be effectively mined, the accuracy and generalization ability of antibacterial peptide activity and MIC value prediction are remarkably improved, and a reliable calculation tool is provided for high-throughput screening and rational design of the antibacterial peptide.

Methods and systems for performing a clinical prediction are provided. In one example, the method comprises: receiving first molecular data of a patient, the first molecular data including at least gene expressions of the patient; receiving first biopsy image data of the patient; processing, using a machine learning model, the first molecular data and the first biopsy image data to perform a clinical prediction of the patient's response to a treatment, wherein the machine learning model is generated or updated based on second molecular data including at least gene expressions and second biopsy image data of a plurality of patients; and generating an output of the clinical prediction.

The invention discloses a crested ibis habitat ecological quality trend analysis method and system, and relates to ecological quality monitoring. The method comprises the following steps: S1, constructing an intelligent sensing network, synchronously obtaining multi-source data of a habitat, identifying activity events of crested ibis, and generating a multi-dimensional habitat parameter table; s2, collecting environmental samples, and generating a microbial functional gene abundance matrix through metagenome sequencing and bioinformatics analysis; s3, taking the activity events of the crested ibis as behavior tags, and generating habitat function health indexes by coupling the parameter table and the matrix training machine learning prediction model; s4, performing spatial interpolation and trend analysis based on the habitat function health index to generate an ecological quality space-time evolution graph; and S5, based on the ecological quality space-time evolution graph, performing quantitative analysis by using a spatial differentiation statistical model, and generating a trend analysis report. By fusing multi-source data, real-time dynamic evaluation of habitat ecological quality and quantitative analysis of driving factors are realized, and a direct decision basis is provided for accurate protection.

The invention discloses a bidirectional reversible conversion method and system between a peptide molecule SMILES and a sequence expression. The core innovation lies in that a new sequence description syntax is defined to retain information of a polypeptide special bond and specific modification of amino acid; a main chain atom index and adjacency traversal topology identification algorithm is adopted, and end group and topology integrated detection and coding are carried out; a residue recognition algorithm for main chain cutting and template library matching is compatible with any standard or non-standard amino acid residues, an extensible end group library / monomer template library and an automatic increment mechanism, and automatic recognition and sequence annotation of S-S disulfide bonds; the invention relates to a high-fidelity assembly algorithm of HELM anchor points and topology aware cyclic peptide processing. The method solves the problems of incapability of supporting a complex polypeptide topological structure, poor reversibility, insufficient expansibility of a monomer library and the like in the prior art, can be widely applied to scenes of quantitative structure-activity relationship model construction, large-scale polypeptide data cleaning and the like, and has remarkable practicability and innovativeness.

The invention relates to a spontaneous cerebral hemorrhage postoperative intermuscular venous thrombosis risk prediction system based on a molecular mechanism and multi-modal data and a use method thereof, image space features are extracted through a multi-modal data level CNN to be complementary with time sequence features captured and detected by Transform, and support is provided for blood coagulation-endothelium-fibrinolysis three-channel activation degree calculation; a knowledge graph alignment module of a pathology and data level maps the cross-modal features to a unified semantic space, and corrects data conflicts with pathology time sequence constraints; and channel-space-time triple attention linkage at a space-time level is carried out, and a high-risk area is accurately marked. In a path driving layer, three-path activation degrees cooperatively adjust a dynamic time window, a single-path standard-exceeding triggering monitoring interval is shortened, and a double-path standard-exceeding amplitude is multiplied; and in a closed-loop feedback layer, system parameters are updated through confidence, pathological probability and conflict signal recursion, real-time calibration of a pathological mechanism and data is realized, and thrombus risk prevention and control efficiency is comprehensively improved.

To provide a pharmaceutical composition for treating a cancer patient having a tumor having a total passenger gene mutation amount larger than the background mutation amount of the tumor.SOLUTION: A pharmaceutical composition for treating a subject having a tumor with a total passenger gene mutation load that is greater than the background mutation load of the tumor, wherein the background mutation load has been determined based on randomly selected genes of the tumor, comprising antibodies that bind to PD1 as an active ingredient. Antibodies that bind PD1 comprise a heavy chain variable region (HCVR) comprising the amino acid sequence of SEQ ID NO: 21 and / or comprise a light chain variable region (LCVR) comprising the amino acid sequence of SEQ ID NO: 22.SELECTED DRAWING: Figure 1

Disclosed herein are methods for sequencing, comprising, providing a sample, wherein said sample comprises a plurality of nucleic acid (NA) molecules, isolating said plurality of NA molecules from said sample, amplifying said plurality of NA molecules, subjecting said plurality of NA molecules to one or more amplification reactions to generate a plurality of cDNA molecules, and sequencing said plurality of cDNA molecules or derivatives thereof. Also disclosed herein are systems, comprising, a processor, and a non-transitory computer readable storage medium encoded with a computer program that causes said processor to providing a sample, wherein said sample comprises a plurality of NA molecules, isolating said plurality of NA molecules from said sample, amplifying said plurality of NA molecules, subjecting said plurality of nucleic acid molecules to one or more amplification reactions to generate a plurality of cDNA molecules, and sequencing said plurality of cDNA molecules or derivatives thereof.

The embodiment of the invention provides an end-to-end B cell clone pedigree forest construction method and related equipment, and can be applied to the technical field of data processing. According to the method, a plurality of obtained receptor sequencing sequences are subjected to germline comparison identification to obtain a first test Fv sequence corresponding to each receptor sequencing sequence, and a germline Fv sequence corresponding to each receptor sequencing sequence is generated; performing integrity filtering on the first test Fv sequence, performing clone type division to obtain a plurality of first clone type sets, constructing corresponding first evolutionary trees to form a first pedigree forest on the basis of a second clone type set contract type conversion probability, and performing node optimization on all the first evolutionary trees to obtain a second pedigree forest; and after it is determined that the homotype category conversion probability after updating based on all the second evolutionary trees meets the preset requirement, visualization processing is performed on all the second evolutionary trees, so that the systematic cognition efficiency of related personnel on the adaptive immune response mechanism can be improved.

The invention discloses a positioning method of trachinotus ovatus growth trait related QTL (quantitative trait loci) based on 2b-RAD technology and application, the method uses 2b-RAD technology to perform sequencing on 300 trachinotus ovatus full-sib F1 generation and male and female parent individuals and develop SNP (single nucleotide polymorphism) markers, constructs a trachinotus ovatus high-density genetic linkage map, performs linkage positioning analysis in combination with 8 growth phenotype data, and finds that the trachinotus ovatus growth trait related QTL is found. 85 stable QTLs associated with the growth traits are screened out, and the stable QTLs contain 763 SNP sites. The invention also discloses an application of the method in a genetic map of trachinotus ovatus growth character positioning or molecular marker-assisted breeding of trachinotus ovatus, and the SNP molecular markers of trachinotus ovatus can be applied in genetic map construction and growth character positioning. Particularly, the method has a good application prospect in trachinotus ovatus growth trait molecular marker assisted breeding.

The invention relates to the technical field of biology, in particular to a signal noise reduction processing method and system, and the method comprises the steps: collecting an original ion current signal, and constructing an original ion current signal segment and a training set associated with a pure ion current signal segment corresponding to the original ion current signal segment; constructing a noise reduction neural network model, and defining a loss function for the noise reduction neural network model; training trainable parameters of the noise reduction neural network model through the training set and the loss function so as to complete training of the noise reduction neural network; inputting all to-be-detected original ion current signal segments of to-be-detected original ion current signals into the trained noise reduction neural network model, and outputting corresponding to-be-detected noise reduction ion current signal segments; and splicing all the to-be-detected noise reduction ion current signal segments to obtain a complete to-be-detected noise reduction ion current signal. According to the invention, the noise in the original ion current signal can be effectively removed, and the distortion of the ion current signal is avoided, so that the recognition effect of the basic group is ensured.

The invention discloses a drug perturbation diffusion prediction method fused with cell description, and relates to the technical field of drug perturbation prediction.The method comprises the steps that firstly, a cell perturbation transcriptome database is preprocessed, and a cell-drug combination containing drug characteristics, cell line gene expression and cell line description characteristics is obtained to serve as training data; and then constructing a drug disturbance prediction diffusion model based on cell description, training the drug disturbance prediction diffusion model by using the training data, and finally inputting Gaussian white noise, cell line gene expression before disturbance, drug characteristics and cell line description characteristics into the trained model to predict cell line gene expression after drug disturbance. According to the method, cell line description characteristics are introduced, so that the perception capability of the model on intercellular biological differences is enhanced, and the generalization performance of cross-drug and cross-cell lines is improved.

The invention discloses an individual breast cancer risk detection method based on HRR pathway related genes, relates to the technical field of breast cancer risk detection, and aims to solve the problems of inaccurate breast cancer risk detection effect and unobvious subsequent treatment effect. According to the method, genetic typing and clinical data are combined to establish an adaptive model, relative risks are accurately calculated and graded, differential early warning and intervention schemes are formulated for low, medium and high risks, precision from risk detection to intervention is achieved, excessive medical treatment or insufficient intervention is avoided, prevention and control pertinence is improved, a dynamic tracking and optimization mechanism is established, and the risk detection accuracy is improved. Tracking frequency and indexes are set according to risk levels, intervention effects are evaluated regularly, schemes are adjusted, a'detection-intervention-tracking-optimization 'closed loop is formed, it is ensured that intervention measures continuously adapt to individual conditions, long-term risk management and control effectiveness is improved, and breast cancer prevention and control are promoted to be upgraded from static management to dynamic management.

The invention belongs to the technical field of essential gene prediction, and particularly relates to an essential gene prediction method based on DNA large model and time-frequency domain deep learning fusion, and the method comprises the steps: taking a domain DNA large model as a core representation layer, and obtaining special gene representation through cross-species corpus pre-training and task fine tuning; a T-Block and F-Block dual-channel time-frequency fusion structure is adopted, and the local dependence and long-range regulation relation of a gene sequence is synchronously captured by expanding DFT (Discrete Fourier Transform), complex value attention and iDFT (Initial Discrete Fourier Transform) conversion; designing an efficient modeling reasoning scheme of sliding window slices and gene-level aggregation aiming at an ultra-long sequence; in combination with class imbalance and a noise robust training strategy, cross-cell line / cross-platform transferable threshold output is realized through temperature scaling calibration, an uncertainty quantization and structured interface is matched, and drug target screening and experimental design decision are supported. The system supports the realization of multiple programming languages, and can complete low-delay end-to-end reasoning in a conventional hardware environment.

The invention provides a full-length gene sequence modeling method and system based on a neural network, and the method comprises the steps: constructing a first expression matrix for initial single-cell RNA sequencing data, and carrying out the quality control transformation of the first expression matrix to obtain a second expression matrix; inputting the second expression matrix into a preset binning embedding module to obtain a binning embedding matrix; maintaining and loading a gene pathway set through a knowledge base and a mapping module to obtain a binary mask matrix, and performing mask processing on the binning embedded matrix based on the binary mask matrix to obtain a pathway mask matrix; the path mask matrix is input into a preset attention state space model, the attention state space model comprises an encoder module, a jump connection module and a decoder module which are arranged in sequence, and a reconstruction tensor is output through the decoder module. According to the scheme, an efficient and extensible whole-gene annotation method is provided, and whole-gene expression input can be processed while the calculation efficiency is kept.

The invention discloses an AI-based gene data analysis system. The system comprises a plurality of omics data matrixes; local association pattern mining is performed on the multi-omics data matrix through a 1D-CNN one-dimensional convolutional neural network, a topological structure of a gene network is identified through continuous coherence analysis, dynamic weights are allocated to sequence features and a topological feature matrix by using a dynamic attention mechanism, and weighted multi-scale feature vectors are output; establishing a multi-modal fusion model based on a Transform architecture to fuse the multi-scale feature vectors, performing fine adjustment on the adaptive disease data set by using the general genome feature of a pre-training model, and outputting a fused feature vector; and inputting the fusion feature vector into an MLP multilayer perceptron for disease risk prediction, generating a disease risk prediction index in combination with an SHAP algorithm, and generating an auxiliary decision scheme according to the prediction index. And the accuracy and generalization ability of disease risk classification are effectively improved.

The invention relates to a motif search-based enzyme element deep learning mining method and system and application thereof, and the method comprises the following steps: determining at least one conservative motif according to the structure positioning requirement of a target enzyme; scanning in a pre-constructed large-scale protein amino acid sequence local database, and obtaining an amino acid sequence set corresponding to the conservative motif through motif search as a seed protein amino acid sequence set; performing fine adjustment on the pre-trained protein large language model; combining the fine-tuned protein large language model with a reference high-speed framework, performing multiple rounds of iterative mining, and expanding a candidate sequence set in each round by adopting a union set retention strategy and a clustering sampling strategy; and screening and filtering the candidate sequence set obtained by iterative mining by using a conservative motif to obtain a final candidate enzyme sequence to be subjected to experimental verification. Compared with the prior art, the method has the advantages of being capable of achieving both efficient excavation and excavation reliability.

The invention discloses a gene sequencing sample data matching method based on a micro-fluidic chip, relates to the technical field of gene sequencing sample matching, and aims to solve the problem that the matching rate is reduced due to inaccurate analysis of gene sequencing samples. According to the method, multi-strategy comparison and candidate set screening are adopted, multi-dimensional comparison of sequences, variation and functions is combined, the accuracy is improved, the adaptability to complex samples is enhanced through quantitative index and biological verification evaluation and dynamic optimization of comparison strategies, the matching reliability and practical value are remarkably improved, the clinical and scientific research diversified requirements are met, and the method is worthy of popularization and application. Sample types such as blood and cells are adapted through a differential lysis strategy, impurities such as proteins and salts are removed through stepped purification, and the nucleic acid concentration and the fragment state are unified in combination with standardized treatment, so that interference is reduced from the source.

The invention relates to the technical field of crop genetic breeding, in particular to a plateau rape adaptive germplasm breeding method based on multi-character collaborative screening, which comprises the following steps of: constructing a multi-character evaluation system covering more than 20 indexes of four categories of agriculture, physiology, quality and stress resistance, and determining character weights by applying principal component analysis, grey relational analysis and analytic hierarchy process; and acquiring data by combining unmanned aerial vehicle multispectral imaging, a ground phenotype platform and a molecular marker technology, and substituting the data into the comprehensive evaluation model for germplasm screening. According to the method, the limitation of traditional single-character breeding is broken through, multi-dimensional collaborative evaluation is achieved, and the plateau rape germplasm breeding efficiency and accuracy are remarkably improved.

The invention discloses a tiny residual focus detection method and system based on ultra-high depth sequencing, and relates to the technical field of tiny residual focus intelligent detection.The tiny residual focus detection method comprises the following steps that on the basis of a sequencing library, splitting is conducted according to a sample index to obtain a to-be-detected sample, and a consensus sequence is obtained according to a molecular identifier of the to-be-detected sample; based on a consensus sequence, filtering out the consensus sequence of which the mass value is less than 25 or the family size is less than 3, and combining a variation type and a distance from a fragment edge as noise introduced into an original nucleic acid molecular chain; a context sequence (context) and a chain direction are used as noise for introducing the capture level of PCR amplification; on the basis of the noise level, the circulating tumor DNA level is estimated in combination with tumor priori knowledge, and the MRD state is determined by detecting the significance of molecular signal sources. According to the invention, the sensitivity and specificity of MRD detection are improved.