2220results about "Sequence analysis" patented technology

The invention provides a molecular identity authentication system and method based on DNA origami framework layered assembly. The molecular identity authentication system comprises a disclosed skeleton chain and a plurality of shared staple chains, the shared staple chain comprises staple chains shared by three groups of single bodies and staple chains shared in pairs, and the staple chains are used as secret keys to be distributed to three participants so as to prepare the three groups of single bodies respectively, and then the three groups of single bodies are combined and spliced into a tripolymer with a dot matrix pattern, so that multi-user collaborative layered chain type assembly is realized. According to the molecular identity authentication system based on DNA origami framework layered assembly, the system combination complexity is improved to the information theory security level, so that the biological information security protection capability is improved, and brute force cracking is prevented.

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 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 application provides a data comparison method, a memory device, and a memory controller. A pre-seeding operation is performed on input data to pre-screen a plurality of candidate matching input data and a plurality of first mismatching input data. A group test is performed on the candidate matching input data to compare the candidate matching input data with a plurality of reference data to generate a matching result, thereby distinguishing a plurality of matching input data and a second mismatching input data from the candidate matching input data, wherein the matching result indicates information about the matching input data which matches the reference data, while the second mismatching input data does not match the reference data.

A genome characterisation system for providing a genome characteristic prediction of a genome of origin associated with an input genomic sequence, the genome characterisation system comprising: an input preparation layer arranged to encode the input genomic sequence in a form suitable for input to a convolutional neural network; a multi-path residual block comprising a plurality of parallel residual routes, each residual route being adapted to receive input data from the input preparation layer and generate residual data corresponding to features of differing length; a self-attention layer arranged to receive residual data from each of the residual routes, generate a set of attention weights based on the residual data and a set of weights, and apply the set of attention weights to the residual data to generate an output tensor comprising data indicative of a relative importance of one or more portions of the input genomic sequence; and an output layer arranged to receive the output tensor from the self-attention layer; and output a likelihood vector indicative of characteristics of the genome of origin.

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 artificial intelligence application, and discloses an enzyme EC number prediction method, and the method comprises the steps: obtaining the sample sequence characteristics of a to-be-predicted sample containing a substrate SMILES sequence and a product SMILES sequence through a target BERT model; constructing a molecular object and feature coding based on atom mapping, atom truncation and sequence analysis, constructing a reaction graph of a to-be-predicted sample, inputting the reaction graph into a target graph isomorphic neural network, and constructing molecular graph features of the to-be-predicted sample based on a recursive neighborhood aggregation mechanism; and fusing the sample sequence features of the to-be-predicted sample with the molecular map features by using a bidirectional cross attention mechanism to obtain multi-modal features, inputting the multi-modal features into the multi-layer perceptron, and obtaining the prediction probability of the enzyme EC number of the to-be-predicted sample. According to the method, efficient and accurate end-to-end prediction of enzyme EC numbering is realized through the multi-dimensional chemical spatial characteristics of the collaborative modeling reaction.

The invention discloses a screening method of hericium erinaceus functional polypeptide based on artificial intelligence assistance and polypeptide or a polypeptide composition. The method comprises the following steps: (1) constructing an AI auxiliary annotation model; (2) extracting hericium erinaceus crude protein; (3) preparing hericium erinaceus protein enzymatic hydrolysate through composite enzymatic hydrolysis; (4) performing polypeptide identification on the hericium erinaceus protein enzymatic hydrolysate to obtain a polypeptide sequence; and (5) carrying out AI auxiliary annotation and polypeptide screening by using a large language model based on a DeepSeek platform so as to obtain a polypeptide sequence with assumed biological activity. The polypeptide sequence and the polypeptide composition which have remarkable ACE inhibition and oxidation resistance dual functions are successfully obtained by utilizing the method disclosed by the invention. Compared with a traditional experience screening path, the method realizes a high-throughput active peptide development process which is targeted in structure, accurate in prediction and capable of verifying a closed loop.

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 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 invention discloses a microorganism comprehensive index construction and intelligent prediction method and system for a water treatment process, and the method comprises the steps: obtaining microorganism samples in different types of water treatment biological systems, obtaining a relative abundance matrix through 16S rRNA high-throughput sequencing, and building a deep learning model of a mapping relation between microorganisms and abundance distribution; carrying out system disturbance simulation by utilizing a deep learning model, evaluating the influence of microbial deletion on the community structure and function, and calculating a structure key score and a function key score; the microbial basic indexes and the key microbial derivative indexes are extracted as key microbial comprehensive indexes, and a pollutant removal performance prediction model with the key microbial comprehensive indexes as input is constructed and used for predicting the pollutant removal efficiency of the complex biological treatment process. According to the method, accurate evaluation and performance prediction of the operation states of different types of complex biological treatment processes can be realized, and a scientific basis and a universal method are provided for intelligent regulation and control of flora.

The invention discloses a bacterial selenoprotein online resource platform, an application method, a terminal and a medium, and relates to the technical field of biological medicine, the online resource platform is deployed on a server, the cloud server is a Ubuntu cloud server, and Nginx, Waitpress and Flask are configured; the server side is in butt joint with a background resource, and the background resource is in butt joint with the constructed bacterial selenoprotein database; the server is in butt joint with a user interface of the front end, the user interface designs a corresponding interface framework and an interaction function based on static resources hosted by Nginx, and the interaction function is used for realizing query, analysis and use of relevant information of the bacterial selenoprotein. According to the method, a convenient online access channel of an integrated database and a real-time data analysis tool are provided for users in related fields, and important support is provided for accurate annotation of selenoprotein genes in a bacterial genome plan.

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.

The invention relates to the technical field of biological information, and discloses a messenger RNA stability prediction method, model, device, equipment and medium, the method comprises the following steps: obtaining target sequence information of a target messenger RNA; acquiring at least two of the following target feature information based on the target sequence information by using a feature extraction module in the messenger RNA stability prediction model: first sequence feature information, Kozak sequence feature information, Motif attention feature information and manual feature information; and predicting the stability of the target messenger RNA based on the target feature information by using a prediction head in the messenger RNA stability prediction model. According to the method, the mRNA stability is predicted by fusing the universal sequence feature of the mRNA, the Kozak sequence feature of the learnable position weight, the Motif attention feature based on the hash k-mer and the manual feature, and the accuracy of mRNA stability prediction is improved.

The invention relates to the technical field of health evaluation, in particular to a group image health evaluation method and system based on intestinal flora, and the method comprises the steps: collecting intestinal flora samples of a target group through a standardized process, and obtaining microbial sequence information through a high-throughput sequencing technology; microflora diversity characteristics and core flora characteristics are extracted through strain identification and relative abundance analysis, individual flora characteristics are compared with a healthy population reference database, health state scores are calculated, health grades are divided, population health grade distribution is counted, and the population health grade distribution is calculated. And generating a group health evaluation report containing a visual chart and text analysis. According to the method, a complete technical system from sample collection to health strategy making is established, systematic evaluation of the group health state from the perspective of intestinal flora is achieved, and the limitation of a traditional method in group health early evaluation is overcome. The method is suitable for health monitoring of different scales of groups, and provides effective technical support for public health management and health service.

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 relates to the technical field of biomedicine, in particular to a method for constructing a plasma ctDNA organ distribution characteristic spectrum of advanced colorectal cancer. The method comprises the following steps: collecting a peripheral blood sample at multiple time points, separating plasma by adopting a double-centrifugal method, and extracting circulating tumor DNA (Deoxyribose Nucleic Acid); carrying out whole exome sequencing based on ctDNA to obtain genome variation information and calculating variation allele frequency, and synchronously detecting the expression quantity of immune-related proteins by adopting an Olink proteomics technology; integrating the genome data, the protein expression data and the clinical pathological features, and constructing a multi-dimensional feature data matrix; and taking the organ metastasis condition confirmed by iconography as a supervision label, training a model by applying a machine learning algorithm, screening key prediction factors, constructing a quantitative prediction model, and finally generating a visual organ metastasis tendency prediction map. According to the method, early and accurate prediction of the advanced colorectal cancer organ metastasis tendency is realized through multi-omics data collaborative analysis and machine learning modeling.

Provided is a method for screening myocardial therapeutic targets for ischemic heart failure by using single-cell sequencing, which method comprises the following steps: S1, sample preparation; S2, construction of a single-cell expression matrix; S3, cell quality control; S4, cell type annotation; S5, cell communication analysis; and S6, co-expression network analysis. The provided method for screening myocardial therapeutic targets for ischemic heart failure by using single-cell sequencing comprises performing single-cell sequencing on hearts of healthy mice and IHF mice, screening for cell types with significant differences in cardiac transcriptional profiles of the healthy mice and IHF mice, then exploring interaction characteristics of various types of cells in malignant fibrotic IHF hearts, revealing potential regulatory modules and pathways related to malignant myocardial fibrosis in single-cell expression data of IHF hearts, and performing screening to obtain Pdgfb and Tnfsf12 genes which can be used as therapeutic targets for treating myocardial fibrosis in ischemic heart failure.

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 present disclosure provides methods and systems for identifying transcriptional regulatory modules (e.g., in non-coding portions of the genome), predicting gene regulation and expression, e.g., effects of non-coding mutations or chromosome rearrangements on the regulation and expression of the target genes, and designing and using modified regulatory sequences.

A tailored interactive language learning system that teaches an individualized set of vocabulary words to users through interactive avatars and stories. The interaction is modeled through probabilistic rules in a semantic network and neural network having objects and relations. Dialog and narration is generated dynamically based on the state of the interactive story model using phrasal rewrite rules and neural network implementiung a four-valued logic system in which truth values of the objects and relations are encoded as true, false, defined, and undefined in a single memory array.

A wafer-on-wafer formed memory and logic device can enable high bandwidth transmission of data directly between a memory die and a logic die. The memory die can be formed as one of many memory dies on a first semiconductor wafer. The logic die can be formed as one of many logic dies on a second semiconductor wafer. The first and second wafers can be bonded via a wafer-on-wafer bonding process. The memory and logic device can be singulated from the bonded first and second wafers.

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 laying hen genetic disease molecular marker screening system based on data fusion and AI prediction, the system comprises six modules, a multi-omics data acquisition module obtains laying hen genome and transcriptome data, and a FineDataLink data fusion module carries out feature alignment and association mapping to generate a fusion feature matrix; the dynamic time sequence diagram neural network processing module constructs a time sequence association diagram and outputs a time sequence feature vector, and the attention enhancement deep forest analysis module evaluates feature importance and outputs a screening result; the federal variation auto-encoder modeling module constructs a federal training framework to generate a molecular marker probability distribution model, and finally the molecular marker screening output module extracts key molecular markers. The system realizes deep fusion of multi-omics data and efficient application of an AI algorithm through multi-module cooperation, improves the molecular marker screening efficiency and accuracy, and provides technical support for disease-resistant breeding of laying hens.

A wafer-on-wafer bonded memory and logic device can enable high bandwidth transmission of data directly between a memory die and a logic die. A memory device formed on a memory die can include many global input / output lines and many arrays of memory cells. Each array of memory cells can include respective local input / output (LIO) lines coupled to a global input / output line. A logic device can be formed on a logic die. A bond, formed between the memory die and the logic die via a wafer-on-wafer bonding process, can couple the many global input / output lines to the logic device.

The invention discloses a protein phase separation characteristic prediction method based on artificial intelligence, particularly relates to the field of protein phase separation characteristic prediction, and is used for solving the problem that an existing method is difficult to fuse protein internal residue interaction structure characteristics, population heritable variation information and experimental phenotype data at the same time in a large-scale missense mutation evaluation scene. And the prediction accuracy and the distinguishing capability are insufficient. The method comprises the following steps: acquiring a wild-type amino acid sequence and population variation data of a target protein, constructing a residue interaction network, identifying a phase separation core interaction cluster, quantifying a network topology disturbance degree caused by missense mutation, combining allele frequency information, constructing a mutation instance graph as input, and calculating the mutation degree of the target protein. And learning and predicting the influence of the mutation on the phase separation characteristic of the protein by using a graph neural network, thereby outputting the phase separation characteristic influence score of each mutation and sorting, and realizing the efficient screening and evaluation of the phase separation related high-risk mutation.

Disclosed herein are systems, methods, and compositions for treating a subject diagnosed with, or suffering from cancer. In some embodiments, the method comprises determining whether a tumor sample from the subject includes a cytotoxic gene signature, and treating the subject based on the determination. In some embodiments, the subject has or is suspected of having a loss of heterozygosity in human leukocyte antigen (HLA) class I genes. In some embodiments, the therapy comprises one or more checkpoint inhibitors. In some embodiments, the cancer is colorectal, uterine, stomach, lung, skin, head or neck, or non-small cell lung carcinoma.

The invention belongs to the technical field of biology, and particularly relates to a receptor peptidomimetic capable of improving the breeding efficiency of butter crabs and application of the receptor peptidomimetic. The optimal binding conformation is screened according to the sequence prediction binding conformation of the vitellogen and the receptor of the vitellogen, the hotspot residues are screened according to the optimal binding conformation, and the binding energy change before and after mutation of the hotspot residues is calculated to determine the receptor peptidomimetic sequence. The receptor peptidomimetic can obviously slow down the development speed of the ovary of the blue crab and effectively induce the formation of the butter crab by utilizing the specificity of specific recognition of the vitellogenin of the blue crab and the receptor, so that the butter crab product can be produced in a planned manner.