82results about "Evolutionary biology" patented technology

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

The invention discloses a multi-layer heterogeneous network unicellular organism network inference method based on meta-path enhancement, which mainly comprises a gene regulation knowledge base enhanced multi-layer heterogeneous network construction module for integrating an external gene interaction network and multiple omics data such as scRNA-seq, scATAC-seq, ST and the like; constructing a single-cell multi-omics multilayer heterogeneous network containing cell-cell, cell-gene and gene-gene relationships, and fusing spatial constraints to consider cell positions and tissue structures; and the feature enhancement module based on the meta-path explores complex semantics of the network by designing a multi-hop meta-path mode, designs an adaptive multi-view learning framework and a multi-round enhancement mechanism, and optimizes feature representation by using cell-gene interaction and cross-modal attention fusion. The unicellular biological network can be effectively deduced, the deduction accuracy and biological interpretation are remarkably improved, the method plays an important role in understanding the cell biological process, developing and treating diseases and the like, has good expandability, and can further integrate multi-modal omics data such as proteomics and metabonomics.

The invention relates to the technical field of biostatistics, and discloses a noise language frequency hearing impairment prediction system based on a GEE model and genetic characteristics, and the system comprises a data collection module which collects follow-up visit data of a worker; the data processing module is used for generating a standardized modeling data set; the feature screening module is used for screening features through a statistical learning method to obtain a key feature subset; the time-varying interaction module is used for constructing a generalized estimation equation model and outputting regression coefficient estimation; the risk prediction module outputs the risk probability and the risk layering result of the individual; the decision support module is used for generating hearing protection suggestions of the individuals; according to the method, the generalized estimation equation model is constructed to process follow-up data of occupational noise exposure workers, so that the accuracy and the stability of language frequency hearing loss risk prediction are improved, a basis is provided for formulating a personalized hearing protection scheme and recommending a proper hearing protection device, and the method is suitable for popularization and application. And the transformation of occupational hearing loss from passive treatment to active prevention is facilitated.

Provided are a method and system for predicting an activation potency of agonist molecules on G Protein-Coupled Receptors (GPCRs). The method includes: blindly speculating complex structures formed by binding of a ligand to an activated receptor structure and an inactivated receptor structure, respectively, via global molecular docking; extracting an initial path enabling an inactivated complex structure to be activated to an activated complex structure based on an enhanced sampling algorithm; searching for a minimum free energy path closest to the initial path by applying an automatic path optimization algorithm; calculating a free energy distribution curve along the minimum free energy path by employing umbrella sampling and determining an energy barrier height and a free energy difference before and after activation, thereby determining the activation potency of the ligand structure on the GPCRs.

The application discloses an antiviral peptide prediction method and system based on federated learning and a neural network, utilizes multiple independent data sets to respectively perform training on a prediction model, constructs a double-channel deep neural network through the model, extracts features of different dimensions from original variable-length sequence data, analyzes peptide segment sequences from two aspects of sequence data and evolution levels, and finally transmits model gradients and parameters obtained to an intermediate server through encryption technology; the server aggregates the transmitted model parameters to form an intermediate model without contacting the data itself; the server transmits parameters of the intermediate model to participants, completes updating of participant models, and the above steps are circularly performed until model training is completed. The combination of federated learning and the neural network can accurately and quickly predict the antiviral activity of peptide segments under the premise of guaranteeing data security.

The invention discloses a poultry breeding pedigree coding method. The method comprises the following steps: firstly, determining strain codes of individuals; determining the appearance codes of the individuals; determining batch codes of the individuals; determining family numbers of the individuals; determining individual type codes of the individuals; determining individual sequence numbers of the individuals; generating a check code; combining the strain code, the provenance code, the batch code, the family number, the individual type code, the individual sequence number and the check code in sequence to form an individual code; according to the method, the structured coding rule containing information such as strains, generations, batches, families, types, individual serial numbers and the like is constructed, so that the information capacity borne by a single code is greatly improved, and key individual identity information which originally needs to depend on database query can be directly reflected in a coding character string; therefore, the problem that the traditional serial number information amount is too low is solved.

The application provides a global ancestor estimation method and system based on principal component analysis, which comprises the following steps: based on the principal component analysis method, according to single nucleotide mutation data, projecting the principal components of the to-be-tested offspring samples and the principal components of the ancestral samples into the same two-dimensional plane, and dividing the projected two-dimensional plane into multiple corresponding population regions; fitting the population region of each ancestral sample to obtain an ancestral population fitting region, and obtaining the region density of each ancestral population fitting region; extending each ancestral population fitting region outward at the same proportion, stopping the outward extension when any ancestral population fitting region contains a to-be-tested offspring sample, and obtaining the ancestral population proportion of the current to-be-tested offspring sample according to the region area after the extension and the region density. The application directly calculates the proportion of the corresponding ancestor in the offspring individual or population through the principal component analysis result, and has high accuracy.

An example computer-implemented method includes receiving agricultural data records comprising a first set of yield properties for a first set of seeds grown in a first set of environments, and receiving genetic feature data related to a second set of seeds. The method further includes generating a second set of yield properties for the second set of seeds associated with a second set of environments by applying a model using the genetic feature data and the agricultural data records. In addition, the method includes determining predicted yield performance for a third set of seeds associated with one or more target environments by applying the second set of yield properties, and generating seed recommendations for the one or more target environments based on the predicted yield performance for the third set of seeds. In the present example, the method also includes causing display, on a display device communicatively coupled to the server computer system, the seed recommendations.

The invention relates to the technical field of agricultural breeding, and discloses a variety screening and promoting method based on partial repeated test design, which comprises the following steps: step 1, variety grouping and sowing quantity preparation: determining the number of repeated varieties and non-repeated varieties according to the total number of varieties to be detected and a preset proportion, wherein the repeated variety is planted in the field for at least two times, the non-repeated variety is planted in the field for one time, and the seeding rate of the corresponding variety is prepared according to the planting times; step 2, experimental plot planning: dividing an experimental plot into latticed cells with clear row numbers and column numbers; according to the method, partial repetition design is combined with a space analysis model based on row and column information. Through a mixed linear model, whether the variety is repeated or not is used as a fixed effect to correct group level difference, the variety is used as a random effect to estimate a real breeding value, and a first-order autoregression structure is used for effectively correcting errors caused by field microenvironment heterogeneity.

Methods and systems are disclosed for covariate prediction from methylation features. A system trains methylation state models that are configured to regress one or more methylation features at a genomic region based on covariates for a given sample. The system utilizes the methylation state models to determine information gain of genomic regions in predicting covariates of interest. The system may, based on the information gain, identify covariate-informative genomic regions. The system trains a covariate prediction model using non-cancer training samples with reported covariate label(s) and methylation features at a plurality of covariate-informative genomic regions. The system may deploy the covariate prediction model for sample swap detection. Additionally, the system may utilize prediction(s) from covariate prediction model(s) to serve as a feature to cancer classification.

Disclosed is a configuration for determining a genotyping label composition of a target individual using direct acyclic paths. The configuration includes receiving a phased genotype of the target individual, including a first haplotype and a second haplotype. The configuration initiates a full-ethnicity hidden Markov model (HMM) including nodes with a set of ethnicity labels. The first haplotype is input to determine a first subset of ethnicity labels that match the first haplotype. The second haplotype is input to determine a second subset of ethnicity labels that match the second haplotype. The first and second subsets of ethnicity labels are combined to create a candidate subset of ethnicity labels for the target individual. The configuration initiates a simplified HMM with nodes from the candidate subset of ethnicity labels. The phased genotype of the target individual is input to the simplified HMM to determine genotyping label composition of the target individual.

Provided herein are methods of determining tumoral sensitivity to hormonal (endocrine) therapy based upon an index of estrogen receptor (ER)- and progesterone receptor (PR)-related genes, referred to as the sensitivity to endocrine therapy index (SETER / PR index), and may have additional consideration for the proportion of ER gene (ESR1) RNA transcripts that contain a mutation relative to the value of the SETER / PR index. Further provided are methods of treating breast cancer patients determined to be sensitive to an endocrine therapy by the SETER / PR index.

The technology disclosed relates to generating species-differentiable evolutionary profiles using a weighting logic. In particular, the technology disclosed relates to determining a weighted summary statistic for a given residue category at a given position in a multiple sequence alignment based on one or more weights of one or more sequences in the multiple sequence alignment that have a residue of the given residue category at the given position.

The present invention relates to the field of genetic engineering. Specifically, the present invention relates to cytosine deaminase and use thereof in base editing. More specifically, the present invention relates to a base editing system based on a newly identified cytosine deaminase, a method for base editing a target sequence in the genome of an organism (e.g., a plant) using the base editing system, and a genetically modified organism (e.g., a plant) produced by the method and progenies thereof.

The computer-implemented method includes storing a trained machine learning model, the machine learning model including a predictor sub-model and a smoother sub-model, the machine learning model being trained based on segments of a training genome sequence for which ancestral origin is known; receiving data representing an input genome sequence of a subject, the input genome sequence comprising a plurality of segments including a plurality of single nucleotide polymorphism (SNP) sites of the genome of the subject, each segment including an array of SNP values ​​at the SNP sites, each SNP value identifying a variant at the SNP site; determining an initial ancestral origin estimate for each segment of SNP values ​​using the predictor sub-model and based on the data; and performing a smoothing operation over the initial ancestral origin estimates with the smoother sub-model for each segment to obtain a final prediction result for the ancestral origin of the segment.

This application provides an end-to-end method and related equipment for constructing a B-cell clonal lineage forest, applicable to the field of data processing technology. This application identifies the first test Fv sequence corresponding to each receptor sequencing sequence by performing germline alignment on multiple acquired receptor sequencing sequences, and generates a germline Fv sequence corresponding to each receptor sequencing sequence. Then, the first test Fv sequences are filtered for integrity and further classified into several first clonal type sets. Based on these second clonal type sets and the same-type class conversion probability, corresponding first phylogenetic trees are constructed to form a first lineage forest. Node optimization is performed on all first phylogenetic trees. After confirming that the same-type class conversion probability meets preset requirements after updating all second phylogenetic trees, all second phylogenetic trees are visualized, thereby improving the efficiency of relevant personnel's systematic understanding of adaptive immune response mechanisms.

The invention provides a method of characterising a DNA sample obtained from a tumour, the method including the steps of: determining the presence or absence of a plurality of base substitution signatures, rearrangement signatures and indel signatures in the sample and copy number profiles for the sample; generating, from the presence or absence of said plurality of base substitution signatures, rearrangement signatures and indel signatures and the copy number profile for the sample, a probabilistic score; and based on said probabilistic score, identifying whether said sample has a high or low likelihood of being homologous recombination (HR)-deficient. Identification of a tumour as HR-deficient may be used to inform treatment choices, for example treatment with a PARP inhibitor or platinum therapy or an anthracycline.

The invention relates to the technical field of black cattle breeding, and discloses a snowflake black cattle intelligent breeding method and system based on an animal model, and the method comprises the steps: collecting pedigree structure data and standardized phenotype data; calculating an additive genetic relationship matrix among snowflake black cattle individuals; establishing a quantitative genetics animal model, and outputting an individual additive genetic effect estimated value and calculating a predicted breeding value through the quantitative genetics animal model under the constraint of the additive genetic relationship matrix; extracting actual phenotype performance data in a subsequent time period; respectively carrying out individual descending sorting and sorting quantile division on the predicted breeding value and the actual phenotype performance data, and extracting a stability index of a target breeding quantile interval; and updating a fixed effect design matrix and variance component parameters of the quantitative genetics animal model, and re-outputting a predicted breeding value and a corresponding snowflake black cattle breeding decision. The stability and reliability of snowflake black cattle breeding are improved.

The present application relates to the technical field of plant breeding, and particularly relates to a method for evaluating the double haploid induction potential of parent combinations in plant breeding. The present application constructs a method for evaluating the double haploid induction potential of parent combinations in plant breeding through genetic recombination simulation, a phenotype prediction model and a comprehensive evaluation function of multi-trait collaborative selection, can quantitatively predict the induction potential of different parent combinations before induction, can simultaneously consider multiple breeding objectives, realize collaborative optimization, and thus help to prioritize the allocation and scale planning of breeding resources. The present application can effectively save induction and identification resources, accelerate the breeding process of excellent varieties, and has a wide application prospect in the breeding of corn, rice, wheat and other plants.

A system for reducing memory processing resources comprises one or more processors configured to generate a plurality of principal components (PCs) corresponding to characteristic data for entities in a dataset to reduce a size of the dataset; generate a score for each entity in the dataset based at least on (i) the PCs, and (ii) weights for a condition of interest; determine a set of alternates that correspond to the condition of interest; determine, using a machine learning model, interactions between the set of alternates for one or more target components corresponding to the object and the scores, wherein the interactions are indicative of a response for the condition of interest; and perform one or more actions.

The application discloses a marine bacteriophage antibacterial lysin prediction method and device, medium and equipment, and relates to the fields of biological medicine and computational biology. The method comprises the following steps: constructing a training data set based on global known antibacterial protein sequences; training a protein sequence classification model based on deep learning by using the antibacterial protein data set to obtain a marine antibacterial lysin prediction model. After data standardization processing, the protein sequence of the bacteriophage to be predicted is input into the protein language model, the input sequence is subjected to self-attention modeling by a ProtT5 module, global structure and function related features are extracted, and a first feature vector is obtained; short sequence local dependency features are extracted by a ProtBERT module, and a second feature vector is obtained; the first and second feature vectors are weighted and summed to generate a fusion feature vector; and the fusion feature vector is subjected to probability prediction, and an antibacterial label and a prediction confidence of the marine bacteriophage sequence to be predicted are output.

The invention belongs to the field of gene breeding, and particularly relates to a cattle 50K liquid phase chip as well as a preparation method and application thereof. The bovine 50K liquid phase chip comprises a primer group and / or a probe for detecting SNP sites on a bovine genome, and the SNP sites designed by the primer group and / or the probe are as shown in a table 1. Compared with the existing cattle genetic typing chip technologies such as Illumina Bovine 100K BeadChip, the cattle 50K liquid phase chip has the remarkable advantages in the aspects of flexibility, accuracy, cost effectiveness and application range, such as high cost performance, high flexibility, rich Index combinations, diversified solutions and wide applicability, and more cattle related varieties are involved.

The application discloses a rice heterosis prediction method, steps comprising: S1, obtaining a plurality of omics data of parent rice and hybrid F1 generation rice, and constructing an omics feature matrix; S2, defining a heterosis label according to the actual measured value of the phenotype of the parent rice and the hybrid F1 generation rice; S3, taking the omics feature matrix obtained in step S1 as input, and taking the heterosis label obtained in step S2 as a prediction target, and training to obtain a plurality of different base learner models; S4, taking the omics feature matrix obtained in step S1 as input, and based on the base learner model obtained in step S3, obtaining the predicted value of the parent rice hybrid offspring, and adopting an ensemble learning strategy to fuse and obtain the heterosis prediction result. The method integrates multiple omics data, adopts a multi-model training and ensemble learning strategy, and performs cross-validation and hyperparameter optimization during model training, can quickly and accurately predict the rice heterosis, significantly shortens the breeding cycle and reduces the breeding cost, and has excellent industrial application value.

The invention provides a formula flora construction method and device, electronic equipment and a storage medium, and belongs to the field of bioinformatics, and the formula flora construction method comprises the following steps: converting a phylogenetic tree constructed by a microbial genome sequence into a weight matrix; performing center logarithm ratio transformation on the original abundance matrix and then inputting the original abundance matrix into an iterative random forest model; correcting the original feature importance into smooth feature importance by using the weight matrix so as to update the feature sampling weight of the next round; and when the model converges, extracting a high-order ecological interaction rule based on a decision path to output a candidate core flora, and further determining a target formula flora. The characteristics are smoothly corrected by introducing the phylogenetic relationship, so that the independence hypothesis of traditional abundance data is broken, the sparsity and false positive interference of high-dimensional sequencing data are effectively overcome, the evolution-driven real inter-species collaborative relationship is accurately captured and reserved, and the accuracy of the method is improved. Therefore, efficient steady-state colonization and precise targeted metabolic regulation of the formula flora in the host microenvironment are guaranteed.

The present invention relates to a microbiological typing method comprising providing: a database of genetic profiles of microbial strains; and a phylogenetic tree, assigning the genetic spectrum to a location in the phylogenetic tree, and assigning a frequency of variation of the spectrum in the tree. The microbial typing comprises the following steps: measuring genetic spectrums of the microorganisms, determining the variation probability of each genetic spectrum in a database relative to the measured genetic spectrums, and calculating the variation probability according to the variation frequency; and determining that the microorganism belongs to a classification unit of the tree if at least the probability of the classification unit is above a threshold. According to the invention, the phylogenetic tree is constructed from the core genome of the microbial species and a predefined set of genetic markers is selected from the auxiliary genome of the microbial species.

The application relates to the technical field of genetic variation interpretation systems, and provides a second-generation sequencing data driven non-phenotype genetic variation intelligent interpretation system and method, which comprises a sequencing data import terminal, a non-phenotype variation annotation terminal, a rare variation structure modeling terminal, an intelligent inference and typing terminal and a result visualization terminal; the sequencing data import terminal is used for importing original format sequencing files and generating variation file information; the non-phenotype variation annotation terminal is used for performing basic annotation and non-phenotype function inference on variations in the variation file information; the rare variation structure modeling terminal is used for extracting non-phenotype rare features and constructing a discrimination model; the intelligent inference and typing terminal is used for performing pathogenic potential scoring and classification on the variations; and the result visualization terminal is used for displaying the original format sequencing files, the variation file information, the discrimination model, the pathogenic potential scoring and the discrimination classification. The application has the effect of improving variation judgment accuracy.

The present application relates to the technical field of crop disease prevention and control, more specifically, to a crop disease prevention and control method and system for improving pesticide use efficiency, soil samples and plant samples are collected, wherein the soil samples and plant samples come from different growth areas and environments, and different growth stages; the microbiome in the soil samples and plant samples is extracted, and the DNA of the microorganisms is extracted; the total DNA of the microorganisms is subjected to high-throughput metagenomic sequencing to obtain sequencing data; according to the sequencing data, a bioinformatics analysis method is used to identify pathogenic bacteria in the microbial community and drug-resistant genes existing in the pathogenic bacteria; a phylogenetic tree of the microbial community is constructed to analyze the succession relationship of the pathogenic bacteria; the pesticide resistance characteristics and change trend of the pathogen are analyzed; according to the analysis results of the microbial phylogenetic tree and the drug-resistant genes, disease prevention and control suggestions are generated to guide the selection and use of pesticides in the disease prevention and control process, greatly reducing the amount of pesticides used and improving the efficiency of disease prevention and control.