3174results about "Genomics" patented technology

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 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 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.

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 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 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 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 relates to the technical field of environmental monitoring and water ecological safety, in particular to a eukaryotic algae outbreak early warning method and system based on genus-level specific recognition. The method comprises the following steps: collecting a water body sample at a monitoring position according to a preset sampling plan, collecting an environment measurement value, and respectively obtaining environment parameter data, a water sample sampling identifier and a sampling timestamp; extracting nucleic acid from the water sample at the monitoring position based on the water sample sampling identifier, performing targeted amplification, and performing high-throughput sequencing at the same time to obtain eDNA original sequencing data; therefore, by constructing the eukaryotic algae outbreak early warning process based on genus-level specific recognition, the problems that in a traditional method, sampling disturbance is uncontrollable, sequence judgment precision is insufficient, trend recognition is lagged, and an early warning link is not transparent are solved, and the accuracy, stability and traceability of early judgment of algae outbreak are improved.

The invention relates to a biological information data processing technology, in particular to a method for analyzing a single-cell Hi-C regulation scale chromatin band, which comprises the following steps: preprocessing single-cell Hi-C data to generate pseudo-batch Hi-C data; performing normalization processing on the pseudo batch Hi-C data to extract a Hi-C contact matrix of each chromosome; identifying and detecting false batch strips from the Hi-C contact matrix; projecting the pseudo batch strips to the original single cell Hi-C data to obtain single cell strips; and carrying out quantitative analysis on the single-cell strip in the original single-cell Hi-C data. According to the method, the regulation and control scale chromatin bands with definite endpoints and directivity can be stably identified, and a band set with remarkable statistics is output.

The present application relates to the field of bioinformatics. In particular, the present application relates to methods and apparatus for speculating variable splicing functionality based on single cell transcriptome data. The method comprises the following steps: determining a variable splicing mode of each gene in a data set in a cell; determining the incidence relation between the variable splicing mode and the gene expression of each gene; a variable splicing mode module is determined according to the incidence relation between the variable splicing modes and the gene expression, and the variable splicing mode module is a variable splicing mode set obtained through clustering according to the correlation between the variable splicing modes and the cell phenotypes; displaying the cell splicing heterogeneity according to the variable splicing mode module; and / or determining a potential regulatory mechanism between the variable splicing mode and the gene expression according to the variable splicing mode module, the potential regulatory mechanism being used for embodying key splicing factors in the gene expression, and a biological approach in which the variable splicing mode affects the cell phenotype.

The present disclosure relates to a multi-phase method for determining a set of candidate genes. During a first phase, the method includes prompting a naïve language model with a plurality of prompts corresponding to a plurality of candidate genes to generate a set of initial scores indicative of each corresponding candidate gene's potential as a biomarker or therapeutic target. During a second phase, the method includes determining, for each candidate gene, a set of relevant documents from a curated document library. The method also includes prompting a further language model using the relevant documents to generate secondary scores. During a third phase, the method includes determining, for each candidate gene, at least one of: a decision classification, a recalibrated score, and a detailed scientific explanation. The method includes determining a final candidate set and conducting a multi-dimensional optimization analysis on each candidate gene of the final candidate set.

The invention discloses a computer-aided drug screening method, system or device based on FBXO2 and PKM2. The invention provides a brand-new method, system or equipment for screening oral squamous cell carcinoma treatment drugs based on FBXO2 and PKM2, provides a tool for new drug development and clinical application for treatment of oral squamous cell carcinoma, and has a wide application prospect.

The invention discloses a single-cell multi-modal data integration method based on an attention mechanism and a graph variation auto-encoder. The method comprises the following steps: step 1, pre-processing multi-modal data and constructing a cell relation graph; step 2, cross-modal adjacency matrix fusion based on multi-head attention; step 3, performing graph variation auto-encoder training and multi-objective optimization; 4, performing multi-target loss calculation and model joint optimization; and 5, carrying out low-dimensional embedding extraction and clustering analysis on the cells. According to the method, single-cell transcriptome and epigenetic group data are fused through a multi-head attention mechanism, and low-dimensional embedding representation of cells is learned by using a graph variation auto-encoder, so that efficient integration and clustering analysis of single-cell multi-modal data are realized.

Methods, computer systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting mRNA characteristics. The system obtains data representing a codon sequence of an mRNA molecule, generates an input token vector by numeric encoding the codon sequence, and generates an embedded feature vector by processing the input token vector using an embedded machine learning model having a first set of model parameters.

Disclosed herein are predictive models for T-cell epitope prediction, B-cell epitope prediction, and protein design wherein a method is implemented for generating a protein variant amino acid sequence of a target protein having one or more modified properties, the method comprising: (a) iteratively sampling an input amino acid sequence of the target protein, and (b) sampling the individual protein score of at least one weighted relative contribution of the single residue mutant input amino acid sequence to the at least one target property across a plurality of other single residue mutant input amino acid sequences to generate a combined protein score, wherein the combined protein score corresponds to the protein variant comprising one or more amino acid mutations of the single residue mutant input amino acid sequences.

The invention relates to a UGT enzyme subtype substrate specificity prediction method and model. On the basis of a directional message passing neural network, graph structure characterization of a small molecule compound and features of specific protein binding sites of UGT enzyme are deeply fused, a bimodal prediction normal form of'molecule + protein binding sites' is designed, a deep learning model is constructed, conversion from compound center prediction to molecule-enzyme binding site comprehensive prediction is achieved, and the prediction accuracy is improved. And accurate classification prediction can be carried out on UGT enzyme substrates and non-substrates.

The invention provides a vaccine target screening system based on calculation model simulation. The vaccine target screening system comprises a multi-source heterogeneous database, wherein the multi-source heterogeneous database integrates and standardizes pathogenic genes, protein structures, literatures and experimental data; the feature calculation module calls a calculation biological model to carry out structural analysis, immunogenicity simulation and stability prediction; the intelligent screening and sorting module applies a multi-objective optimization algorithm to perform parallel evaluation and outputs optimal target spots; a structure iteration optimizer automatically iteratively corrects the optimized target spots to generate a high-potential variant library; and the process suitability simulation module couples the variants with the preparation formula and the process parameters to simulate production storage behaviors and feeds back an optimization target. According to the invention, efficient screening and optimization of vaccine targets can be realized, the accuracy and efficiency of target screening are improved, the research and development cost is reduced, and the research and development process of vaccines is accelerated.

The invention discloses a risk assessment method, system and equipment for idiopathic pulmonary arterial hypertension, and belongs to the field of pulmonary arterial hypertension. According to the method, SNP data containing genotypes and effect values, protein marker expression quantity, metabonomics and clinical data are obtained, the SNP effect values are corrected based on linkage imbalance reference information, and PRS is calculated in combination with the genotypes; constructing a protein expression score by utilizing the site effect value and the expression quantity of the pQTL, and fusing the protein expression score with the PRS to form a target PRS; carrying out dimensionality reduction on metabolome data by adopting sparse coding, extracting sparse coefficients of IPAH related metabolic pathways, and converting the sparse coefficients into metabolic pathway scores; converting the clinical indexes into clinical risk scores; based on the clinical parameter distribution target PRS, the metabolic pathway score and the weight coefficient of the clinical risk score, calculating a risk assessment value; and finally, matching the evaluation value with a preset risk threshold value, and outputting a risk evaluation level. And the IPAH risk assessment accuracy of common people is improved.

The invention discloses a deep learning prediction method fused with multi-modal features, which can accurately identify protein hidden binding sites in a ligand-free (apoo) state. The method comprises the following steps of: firstly, constructing a protein graph by taking residues as nodes and taking C alpha distance less than or equal to 14 as edges, wherein node feature sets comprise amino acid one-hot, secondary structures, atomic attributes, protein language model embedding and BLOSUM62 evolutionary information, and edge features comprise distance and angle similarity; then capturing three-dimensional geometric equivariant features by adopting an equivariant graph neural network (EGNN), and modeling a chemical topological relation by using a graph isomorphic network (GINE) with edge features; eGNN and GINE double-branch feature fusion and global dependence integration are realized through gating cross attention and gating multi-head attention; and finally, inputting the fusion features into a Kolmogorov-Arnold network (KAN) classifier, and predicting whether each residue belongs to a hidden binding site or not. The method can adapt to large-scale conformation change without coordinate alignment, AUC and F1 on a standard data set are remarkably superior to those of an existing method, high robustness and generalization are kept for multi-chain protein and complex conformation, and the method can be widely applied to drug target discovery and structure-driven drug design.

The invention discloses a disease diagnosis method and system based on multi-modal space-frequency domain adaptive fusion, and relates to the field of artificial intelligence and biomedical engineering.The method comprises the steps that multi-modal data are standardized, the unified and standardized multi-modal data are coded, and multi-modal initial feature representation is obtained; after projection and gating alignment and cross-modal interactive attention alignment are carried out on the initial feature representation of each modal, enhanced representations of each modal are obtained, and then the enhanced representations of each modal are fused into a shared feature representation; performing deep feature extraction on the enhanced representation of each mode to obtain deep features of each mode, and performing adaptive multi-domain feature enhancement processing to obtain multi-domain enhanced features of each mode; performing semantic alignment on the multi-domain enhanced features of each mode, and then performing fusion through a hierarchical attention mechanism to obtain fusion features; and the fusion features are input into a diagnosis network for prediction, a disease diagnosis result is obtained, and the intelligent diagnosis precision and robustness of papillary thyroid carcinoma are improved.

The invention discloses the technical field of pathology and space transcriptomics, and particularly relates to a gene expression prediction method and system based on multi-modal comparative learning and a guidance mechanism. Cutting the histological slice image into image blocks according to space coordinates; according to the method, a local convolution branch and a global Transform branch are combined to extract image features, the image features are mapped to a shared potential space through projection, soft contrast, hard contrast and global consistency constraints are introduced into the space, and cross-modal alignment of an image modal and a gene expression modal is realized; an expression prediction head is introduced in the training stage, representation learning is directly guided by a regression signal, and the relation between feature learning and gene expression prediction is broken through; in the inference stage, k-nearest neighbor retrieval and a multi-distance weighted aggregation strategy are combined to infer a gene expression profile of an unknown position. According to the method, the accuracy and robustness of space gene expression prediction can be effectively improved, the tissue space heterogeneity structure is kept, and the method has high clinical application and scientific research and popularization value.

According to one aspect, there is provided an AI-based platform which may include an experiment data set including records that respectively represent an experiment. Each record may indicate at least one hypothesis associated with the experiment and an experiment definition based on the at least one hypothesis. An AI-based agent may be configured to perform an evaluation of respective records of each experiment, and generate, based on the evaluation, at least one observation about the at least one hypothesis associated with the experiment represented by each of the respective records.

A protein interface prediction method based on three-track coding comprises the following steps: combining a fine-tuned protein language model SiteT5 with evolutionary, geometric and statistical features extracted from a sequence, sending the combined features into a three-track coding network, and integrating a cyclic gating module, a multi-resolution aggregation module and a long sequence deformation module to obtain a protein interface prediction model SiteT5; the method comprises the following steps: respectively capturing a time sequence relation, a local mode and long-range dependence among residues, respectively mapping the three codes into different weights, carrying out point multiplication on the three codes, and carrying out aggregation through a multi-view cross attention module; then the protein residues are sent to a three-layer hierarchical interactive learning module, local structure and global dependency are cooperatively mined through an eight-head gating self-attention module and a position-by-position feedforward module, and finally the probability that each protein residue is an interface is obtained through a classifier. According to the invention, a protein-DNA interface, a protein-RNA interface, a protein-protein interface and an antibody-antigen interface can be effectively captured. And the robustness is ensured, and meanwhile, relatively high prediction precision is also shown.

The invention discloses a high-specificity minimal residual disease (MRD) monitoring method and system based on circulating tumor DNA (ctDNA). The method comprises the following steps: receiving tumor tissue sequencing data of an UTUC patient, and generating a double-Panel target list containing personalized and fixed Panel; respectively extracting plasma cfDNA and leukocyte gDNA; performing vacuum concentration, hybrid capture and sequencing on the cfDNA library by using the double Panel lists, and performing deep sequencing on the leukocyte gDNA; constructing an individualized clonal hematopoietic mutation filtering database; actively filtering and rejecting clonal hematopoietic background mutation by utilizing a filtering database; and calculating an MRD load score based on the filtered tumor-derived mutation and outputting a report. The system comprises corresponding modules which are used for automatically executing the process. According to the invention, through cooperation of four major technologies of double-Panel design, process optimization, UMI error correction and active clonal hematopoietic filtration, MRD monitoring with extremely high sensitivity and specificity on UTUC is realized, false positive is significantly reduced, and the kit has drug resistance early warning potential.

Embodiments disclosed herein provide methods, including computer-implemented methods, for designing guide sequence which may be incorporated into custom, large scale guide sequence libraries. The methods require only a list of target genes as input and utilize on target and off target scores to generate an optimal set of guide sequences for a set of target genes. In certain embodiments, the methods may also utilize multi-tissue RNA-sequencing data and / or protein annotation to design targets to genes that are highly expressed and / or contain a functional protein domain. The invention further comprises guide libraries, cells comprising said guide libraries. Computer-implemented embodiments further improve computer system function by reducing excessive user wait time through the use of data structures that reduce search from linear to logarithmic time.

The invention provides an antibacterial peptide generation method constructed based on a recognition model, and the method comprises the steps: updating a dynamic data set, and carrying out the adversarial optimization and repeated iteration of a generator network and a discriminator network based on the updated dynamic data set, thereby guaranteeing the learning of real diversified antibacterial peptide features with high antibacterial activity and low toxicity, and improving the recognition efficiency of the antibacterial peptide. According to the method, the capability of identifying the real antibacterial peptide from the antibacterial peptide data set is improved, the authenticity of the representative amino acid sequence generated by the generator network is synchronously verified in adversarial tuning, and the generation efficiency of the antibacterial peptide is improved.

Embodiments of the disclosure include systems and non-transitory computer readable media for analyzing microscopy images for developing machine learning models for disease modeling. Microscopy images are captured from cells of one or more exposure response phenotypes (ERPs) and further used to train machine learning models. Thus, trained machine learning models can distinguish between microscopy images captured from healthy and diseased samples.

The invention discloses a forest tree genotype-environment interaction modeling method based on multi-modal deep learning, and relates to the technical field of forest tree breeding, the method comprises the following specific steps: multi-modal data acquisition: adopting a high-throughput phenotype platform, a whole genome sequencing technology and soil nutrient detection equipment to acquire multi-modal data; sNP locus genotype data, soil key physicochemical index environmental data and growth-related morphology and biomass parameter phenotype data of forest trees are collected respectively; forest genotype, environment and phenotype multi-modal data are collected through the system, the genotype-environment interaction algorithm and the phenotype prediction model are constructed after preprocessing and fusion, the model can accurately predict forest phenotypes, the breeding screening period is remarkably shortened, the breeding selection precision and efficiency are greatly improved, and the method is suitable for large-scale popularization and application. The method effectively solves the problem that a traditional breeding mode is short in time and efficiency, enables breeding work to respond to market demands and environmental changes more quickly and accurately, and provides powerful support for sustainable development of the forestry industry.