3678results about "Sequence analysis" patented technology

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 discloses a method for evaluating enhancement and release seedling resources based on environmental DNA polymerization analysis. The method comprises the following steps: carrying out gridding partition on a target water area, collecting a water sample through a designed sampling scheme, and carrying out DNA extraction and high-throughput sequencing to obtain species sequence information of each sampling point. Sequencing data is subjected to species identification by using a bioinformatics method, released species are identified, a spatial abundance model is established, and a preliminary distribution map is generated. And establishing a DNA degradation kinetic model in combination with water area environmental parameters, and carrying out reverse correction on abundance distribution. And through a resource inversion model coupled with hydrodynamics, analyzing biomass distribution characteristics and migration laws of the release group, and obtaining a resource evaluation result. And finally, a species environment preference model is constructed based on migration path analysis, an optimal release area is matched in a target water area, a scientific scheme including release point locations, opportunities and quantity is generated, and a whole-process technical support and a decision basis are provided for enhancement and release.

The invention discloses a method and system for predicting juvenile depression based on intestinal flora, and relates to the technical field of bioinformatics and artificial intelligence, and the method comprises the steps: firstly, obtaining an original sequence of a microbiome, carrying out the preprocessing of the original sequence of the microbiome, and obtaining a feature matrix; and screening core flora characteristics with stable trans-folding by adopting characteristic importance evaluation and interpretability analysis based on a gradient boosting decision tree. A mixed weighted graph is constructed based on Spearman correlation and a proximity relationship, and an absolute value of a correlation coefficient is taken as an edge weight and an edge density is adjusted through a threshold adaptive strategy. And finally, through an improved graph attention neural network, based on edge weight attention, layer normalization and random inactivation, enhancing robustness, and adopting adaptive optimization to complete parameter learning. And determining a dynamic classification threshold according to the AUC of the target patient, and outputting a sample discrimination result and confidence. According to the method, the accuracy, stability and biological interpretability of juvenile depression recognition are remarkably 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 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 spatial domain identification method and device based on multi-modal topological consistency, and belongs to the field of transcriptome spatial domain identification, and the method comprises the steps: constructing a multi-layer network which is in one-to-one correspondence with modal information contained in a biological tissue based on spatial transcriptomics data of the biological tissue; extracting a consensus structure feature shared by the multi-layer network and a specific structure feature specific to each layer of network; constructing a cell consistency network of the biological tissue according to the consensus structural features, the specific structural features and the multilayer network; and performing clustering processing on the cell consistency network to obtain recognition results of different spatial domains in the biological tissue. According to the method, the heterogeneity problem among different modal data can be overcome, and the spatial domain recognition effect is better.

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.

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

The invention discloses an antler bone strengthening peptide for promoting bone repair as well as a screening method and application thereof, and belongs to the technical field of traditional Chinese medicinal material polypeptides. And the antler bone strengthening peptide is one of G40, G44 and G45. Grinding and crushing the antler, homogenizing the lysate, performing ultrasonic centrifugation, precipitating protein with methanol, centrifuging and discarding the supernatant; extracting, desalting, and freeze-drying to obtain a antler polypeptide extract; liquid chromatography-mass spectrometry detection, Peaks 8 search software and De Novo method analysis are adopted, according to the conditions that ALC is larger than or equal to 95%, local context is larger than or equal to 95%, Area is larger than or equal to 1000000 and an NCBI database, free polypeptide is obtained through comparison, the biological activity probability, the peptide fragment toxicity and the sensitization of the peptide fragment are predicted, and candidate polypeptide is obtained; synthesizing candidate polypeptides by a solid-phase synthesis method; the influence of the candidate polypeptide on osteoblast proliferation is evaluated, and the antler bone strengthening peptide is screened out. The antler bone strengthening peptide promotes osteoblast proliferation, and reveals a potential bone repair mechanism of the antler bone strengthening peptide.

The invention discloses an identification and analysis method for unknown pathogenic microorganisms, which comprises the following steps: filtering out genome sequences with low integrity, pollution and tag errors, and establishing a high-quality virus identification database; constructing a virus host prediction model through a machine learning algorithm; unknown pathogenic microorganisms are identified and analyzed, potential hosts or pathogenicity of the unknown microorganisms are identified, and whether the unknown microorganisms are unknown pathogenic viruses or bacteria or not is further judged. On the basis of metagenome data analysis, potential unknown pathogenic microorganisms in samples of human bodies, environments and the like can be identified more accurately.

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 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.

The invention relates to the technical field of bioinformatics data processing, in particular to a genome sequence compression method and system. The method comprises the following steps: acquiring genome sequencing data; comparing the sequencing data with a reference genome to determine a difference site; differentiating the difference sites as sequencing errors or real variations through a time sequence difference neural network model; performing differential compression coding according to an identification result; a friendly variation detection format is constructed, and rapid variation query is supported through a multi-level index structure and a variation metadata table. According to the method provided by the invention, the sequencing error and the real variation can be accurately distinguished through the time sequence differential neural network model, and important biological variation information is protected while the compression efficiency is improved by adopting the differential compression coding strategy.

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 belongs to the field of construction of a protein structure on a cryoelectron microscope density map, and relates to a method for constructing a protein structure from a cryoelectron microscope density map by combining de novo modeling and structure prediction, a computer device, a readable storage medium and a program product. According to the method, the atomic probability and the amino acid type are predicted through the three deep neural networks respectively, full-atom optimization is carried out, the output result is combined with the graph theory, optimization and geometric algorithms to jointly assist protein structure modeling, and protein structure information can be mined to the maximum extent. According to the method, de novo modeling can be carried out in the absence of full-length structure data of the protein monomer, integrated modeling can also be carried out under the condition of inputting the full-length structure data of the protein monomer, the application scene is wide, the structure of the protein compound can be automatically constructed in a cryoelectron microscope density map with middle and high resolution, and the method is suitable for popularization and application. And for a region with poor local resolution in a traditional method, the modeling precision can be remarkably improved.

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.

A method for performing local alignment based on a query sequence of DNA and a reference sequence of DNA includes: obtaining a bit matrix H; determining at least one diagonal based on the bit matrix H; for each of the at least one diagonal, calculating an initial score for the diagonal, determining at least one trace region, determining a sub-alignment for each of the at least one trace region, consolidating the diagonal and the sub-alignment respectively of the at least one trace region to obtain an alignment, and obtaining an alignment score based on the initial score and the partial score respectively of the sub-alignment respectively of the at least one trace region; and among each of the at least one alignment thus determined respectively for each of the at least one diagonal, reserving one of the at least one alignment that has the highest alignment score therefrom.

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 method, a system and equipment for detecting internal tandem repeat and a storage medium, and the key points of the technical scheme are as follows: obtaining a first reference sequence according to at least one target exon sequence corresponding to a protooncogene, and obtaining a second reference sequence according to at least one target intron sequence corresponding to the protooncogene; comparing the sequencing data of the to-be-detected sample to the second reference sequence to obtain a first comparison result, extracting an uncompared sequence from the sequencing data according to the first comparison result, and comparing the uncompared sequence to the first reference sequence to obtain a second comparison result; and determining a first detection result according to the second comparison result and a first reference sequence, and performing false positive filtering on the first detection result to obtain a second detection result. According to the invention, false positive can be reduced so as to ensure the accuracy and reliability of subsequent analysis.

A haplotype genome assembly method and device, and related applications. The method comprises: acquiring short-read-length data and long-read-length data obtained by sequencing the same biological sample to be detected; performing error correction on the long-read-length data according to the short-read-length data to obtain error-corrected long-read-length data; performing genome assembly according to the error-corrected long-read-length data to obtain a preliminary assembly sequence; and optimizing the preliminary assembly sequence according to the short-read-length data to obtain a target assembly sequence. The method addresses the technical problem in the related art where obtaining high-quality genome assembly requires the use of three types of sequencing data, resulting in excessive data usage.

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.