25 results about "Gene selection" patented technology

The invention discloses a BERT-based cell type deconvolution method and a BERT-based cell type deconvolution system, which effectively overcome the problems of data noise and unstable gene expression in single cell transcriptome sequencing data, enhance the adaptability to gene-gene interaction in complex single cell data, effectively capture gene-gene complex relationship, and improve the accuracy of gene-gene deconvolution. The problem of dependence on hypervariant gene selection and data dimension reduction in a traditional method is solved, and the generalization ability and robustness of the model are remarkably improved. By means of the design of gene embedding, unstable gene expression is converted into more stable feature expression, the accuracy and robustness of cell type recognition are remarkably improved, and high performance is shown in complex heterogeneous cell sample annotation.

The invention relates to a multi-target gene detection method based on a liquid phase chip, which comprises the following steps: S1, target genes: selecting O-type serum antigens to synthesize key genes; s2, probe design: synthesizing key genes aiming at seven serotype antigens, and designing a specific primer pair; s3, Luminex microsphere coding and coupling: using MagPlex microspheres with different fluorescence codes, and fixing a probe on the surface of the microspheres through a carboxyl-amino coupling method; s4, carrying out multiplex PCR amplification and hybridization detection; s5, a data analysis algorithm is adopted, and a threshold value is set, wherein a negative control MFI mean value + 3 * standard deviation is a Cuff value; and positive judgment: the MFI value of the microsphere corresponding to a certain serotype is greater than or equal to Cuff, and the signal-to-noise ratio (S / N) is greater than or equal to 5. By adopting the technical scheme, the method is based on a Luminex liquid phase chip technology, target gene sequences are amplified through multiple PCR (Polymerase Chain Reaction), and high-flux specific detection on seven serotypes of Escherichia coli is realized by combining microsphere coding probe hybridization and a biotin-streptavidin signal amplification system.

The invention provides a feature gene selection method and system based on deep learning attribution analysis and beam combination optimization, and relates to the technical field of signal analysis, and the method comprises the following steps: obtaining single cell RNA sequencing data and batch RNA sequencing data; the method comprises the following steps: based on single-cell RNA sequencing data, obtaining an initial gene pool aiming at a plurality of disease subtypes through a consensus screening strategy fusing XGBoost multi-dimensional importance measurement and deep learning SHAP attribution analysis; performing first-stage optimization on the gene list of each subtype by taking the initial gene pool as a starting point and adopting beam combination search and based on a global discrimination objective function to obtain a first-stage optimized gene set; integrating the first-stage optimized gene set with the differential expression gene set of the batch RNA sequencing data, and carrying out second-stage optimization by adopting bundle combination search again to obtain a second-stage optimized gene set; and performing cross validation integration on the second-stage optimized gene set, and outputting a final feature gene set.

The present disclosure relates to providing an engineered CHO cell line wherein the two essential metabolic genes are knocked out. Particularly, the present invention relates to a double knockout CHO cell line (DHFR− / − and GS− / −) with disrupted Dihydrofolate Reductase (DHFR) and Glutamine Synthetase (GS) genomic loci. The double knockout CHO cell line (DHFR− / − and GS− / −) being suitable for expression of monoclonal antibodies, dimeric therapeutic proteins, Fab, single chain fragments, or the like. The present disclosure also provides method of generation of a double knock out CHO cell line (DHFR− / − and GS− / −) using gene selection and / or manipulating techniques such as CRISPR / Cas9 system, Zinc Finger Nuclease, TALEN, or the like. The present disclosure further provides method of selection of clones and production of therapeutic proteins of interest with increased titre.

The invention relates to the technical field of biological information, and particularly discloses a multi-modal integration method for unicellular omics based on a linear selective state space. Comprising the following steps: (1) performing quality control on original multi-omics data, and removing low-quality samples; (2) carrying out normalization and hypervariant gene selection on the processed multi-omics data; (3) constructing an omics feature fusion module, integrating common features of multiple omics, constructing an omics feature enhancement module, and retaining and enhancing unique details of each omics; (4) constructing a selective state space fusion module, and integrating the unique information and common features of each group of science; (5) constructing a selective state space enhanced fusion network, and connecting modules in series to form an integral network structure; and (6) clustering the cells and comparing the clustered cells with real labels to obtain clustering performance. According to the method, the transcriptome data and the proteome data can be well integrated, and unique information is reserved while common features of the multi-modal data are extracted.

The present application belongs to the tumor precision medicine and artificial intelligence medical technology field, and particularly relates to a non-small cell lung cancer neoadjuvant immunotherapy efficacy prediction method based on transfer learning. The method first pretreats the non-small cell lung cancer immunotherapy cohort and the neoadjuvant immunotherapy cohort, selects immunotherapy and neoadjuvant immunotherapy characteristic genes, and based thereon, carries out deep learning training on the immunotherapy cohort to obtain a basic model, finally, the basic model is fine-tuned to the neoadjuvant immunotherapy training data set through transfer learning, and finally used to the neoadjuvant immunotherapy cohort to predict the neoadjuvant immunotherapy effect. The present application improves the stability and generalization ability of the model in the small sample neoadjuvant treatment scene through gene selection and transfer fine-tuning, and can be used for clinical auxiliary decision and population stratification.

The invention discloses an efficient SNP marker automatic screening system for dairy cow breeding, relates to the technical field of dairy cow breeding screening, and aims to solve the problems that the scientificity and stability of a screening result are reduced, and the error between genetic improvement efficiency and marker utilization rate is increased. The method comprises the following steps: extracting dairy cow breeding character information and gene site data from a genotype-phenotype database, calculating heritability of each character, screening target characters with high genetic contribution degree, calculating character action characteristics and effect directions of gene sites on the basis of an association relationship between the target characters and SNP sites, quantitatively describing regulation intensity of the gene sites, and screening out the target characters with high genetic contribution degree. Generating a character guiding coefficient according to the character guiding coefficient, screening significant candidate sites in combination with a threshold value, performing cross-character frequency statistics on the candidate sites, calculating a breeding efficiency index in combination with a character correlation weight, and performing hierarchical marking according to the index, so as to realize multi-character comprehensive genetic effect evaluation and automatic screening. The breeding data processing efficiency and the gene selection scientificity are improved.

The invention belongs to the technical field of biology, and relates to prevention and treatment of ladybird beetles, in particular to dsRNA of an insect neuropeptide ecdysis triggering hormone ETH gene and a preparation method and application of the dsRNA. Through transcriptome sequencing, screening to obtain the neuropeptide ETH gene of the ladybird; a fragment with the gene sequence as shown in SEQ ID No.2 is selected for synthesizing dsRNA (dsETH). After 3-day-old larvae dsETH (concentration is greater than or equal to 1 ng / mu L) of the ladybird are fed, more than 70% of the ladybird malforms and cannot eat on the seventh day, more than 70% of the ladybird dies on the tenth day, and the malformation rates of 5 ng / mu L, 10 ng / mu L, 25 ng / mu L and 100 ng / mu L respectively reach 85%, 90%, 95% and 100%. The invention provides a new prevention and treatment target for prevention and treatment of the ladybug 28 based on RNA (Ribonucleic Acid) interference.

The invention discloses a single base editing system for regulating and controlling selective expression of a CaMKII-delta subtype. The system is composed of a Cas9 protein mutant fused with base deaminase and sgRNA for recognizing a CaMKII-delta gene sequence. Wherein the sgRNA guides the fusion protein to realize single base substitution of a splicing donor of a target site intron 15 or a splicing ligand of intron 16 in the CaMKII-delta, the RNA splicing process of the target gene CaMKII-delta is effectively regulated and controlled, the CaMKII-delta 3 subtype is selectively expressed by the CaMKII-delta gene subjected to directional splicing regulation, and meanwhile, the CaMKII-delta 9 is eliminated. The invention further provides application of the base editing system in treating ischemic cardiomyopathy, cardiac CaMKII-delta subtype selective expression is regulated through single base mutation, myocardial damage caused by blood reperfusion is efficiently reversed, and the base editing system is expected to be applied to treatment of ischemic cardiomyopathy.

The invention discloses a method for selecting single nucleotide polymorphism (SNP) for phenotype prediction based on feature importance, which comprises the following steps of: performing missing filling on SNP data, and dividing a sample into a training set and a test set before phenotype-related screening to avoid data leakage; then, by taking the gene as a unit, fitting phenotypes of the SNPs positioned on the promoter, the exon and the intron by adopting a regression model, calculating correlation coefficients and carrying out multiple inspection correction, and taking a significant correlation gene as a candidate; selecting one SNP (Single Nucleotide Polymorphism) from each gene in the candidate genes on the basis of feature importance to form an Important-SNP set; and encoding the set, and inputting the encoded set into a prediction model to obtain a phenotype prediction result. According to the method, the feature dimension is remarkably reduced while the prediction accuracy is maintained or improved, and the method has relatively high interpretability and engineering availability, is suitable for phenotype prediction of crops, can be used as a core marker for design and optimization of a breeding chip, and provides efficient and interpretable technical support for molecular breeding and genome selection.

The invention provides an internal reference gene of hypsizigus marmoreus RT-qPCR and an amplification primer and application thereof, the internal reference gene is one or more of a pATPase gene, an RGTPase gene and a beta eLYase gene, the transcript sequence of the pATPase gene is shown as SEQ ID NO.1, the transcript sequence of the RGTPase gene is shown as SEQ ID NO.2, and the transcript sequence of the beta eLYase gene is shown as SEQ ID NO.3. The invention further provides an application of the internal reference gene of hypsizigus marmoreus RT-qPCR. The invention also provides a primer for amplifying the reference gene and application of the reference gene in gene expression analysis of hypsizigus marmoreus in different growth and development processes. The reference gene provides reliable reference for gene expression analysis of hypsizigus marmoreus in different growth and development stages, the difference between samples and in the samples caused by reference gene selection is reduced to the maximum extent, and a solid foundation is laid for researching key gene expression in the hypsizigus marmoreus development process.

The invention discloses GmBCM1 and GmBCM2 genes for identifying soybean hybrid yellowing and application of the GmBCM1 and GmBCM2 genes, and relates to the GmBCM1 and GmBCM2 genes and application of protein coded by the GmBCM2 genes in soybeans. The open reading frame nucleotide sequences of the GmBCM1 and GmBCM2 genes are respectively shown as SEQ ID NO: 1 and SEQ ID NO: 2 in a sequence table. The GmBCM1 and GmBCM2 genes are applied to soybean molecule assisted breeding, gene selective breeding and AI breeding. The soybean GmBCM1 and GmBCM2 genes cooperatively control soybean hybrid yellowing phenotypes, and it is found that when plant individuals are Gmbcm1 / Gmbcm2 genotypes, soybean plants show phenotypes that the chlorophyll content of mature leaves is reduced, the photosynthetic efficiency of the leaves is reduced and the like. The genotype identification results of GmBCM1 and GmBCM2 related to soybean hybrid yellowing can be applied to soybean molecular assistance, gene selection breeding, AI breeding and other breeding technologies, and play a role in soybean breeding parent material optimization and advanced generation breeding material selection.

The invention discloses a quantitative evaluation system and method for the inflammation level of cells cultured in vitro, and the system employs a modular design, and is composed of a data collection and preprocessing module, a feature gene selection module, a neural network modeling module, and an inflammation index calculation module. The evaluation method comprises the steps of data preparation, original data processing, feature gene screening, machine learning model screening, neural network model architecture, neural network weight analysis, inflammation index calculation and the like. The method has the advantages of high speed, high throughput, low cost, high accuracy, high mobility and high interpretability.

The invention discloses a children long phase prediction method based on multi-modal gene selection, and relates to the technical field of artificial intelligence. The child longphase prediction method based on multi-modal gene method selection comprises the following steps: step 1, data preparation: collecting an open source data set and real portrait data mainly including adult male and female, fully covering race, then preprocessing the data, including image size adjustment, face detection, cutting and alignment correction of five sense organ points, and obtaining a face image; according to the child longphase prediction method based on multi-modal gene method selection, face features and gene information are accurately extracted and processed through a multi-module structure of a model, gene selection is guided by using text data, and finally, accurate child longphase prediction is realized through a proper loss function training model, so that after a parent photo is input, the child longphase prediction accuracy is improved. The predicted child portrait image is automatically generated, and the process is efficient and high in accuracy.

This invention provides a method and application for identifying molecular markers associated with tail fat weight in sheep based on a multi-omics strategy. The method is based on a comprehensive analysis of whole-genome selection signal analysis, whole-genome association analysis of tail fat weight, and epigenomic and transcriptomic sequencing results of tail adipose tissue in sheep with different tail types. The marker is located at position 51760995 on chromosome 13 of the sheep reference genome Oar_rambouillet_v1.0, where an A / C base mutation exists, which is significantly correlated with tail fat weight. Amplification of the genomic region containing the SNP site statistically verified that the tail fat weight and relative tail fat weight of individuals with the AA genotype are significantly lower than those with the CC genotype. By detecting the genotype of the molecular marker, this invention allows for the selection of homozygous AA sheep for breeding in the core population, thereby reducing tail fat deposition and improving the economic benefits of sheep farming.

This invention relates to a method for increasing the yield of polyketide compounds by splitting a modular polyketide synthase gene. (1) Constructing a RedEx gene cassette for splitting the modular polyketide synthase gene; (2) Constructing a target plasmid carrying an amp-ccdB gene selection expression cassette; (3) Constructing a target plasmid carrying an amp-ccdB gene selection expression cassette. C DD-TGA-RBS-ATG- N (3) Target plasmid encoding the DD coding sequence; (4) Construction of recombinant strains; (5) Fermentation culture to obtain polyketide compounds. This invention is the first to discover that the biosynthetic efficiency of host strains carrying the split PKS gene is much higher than that of host strains carrying the natural PKS gene, which can achieve the purpose of increasing the yield of polyketide compounds. Using this method of splitting modular polyketide synthase genes, high-yield butenyl spinosad genetically engineered bacteria and high-yield avermectin genetically engineered bacteria were successfully constructed, which effectively increased the yield of butenyl spinosad and avermectin. The yield of butenyl spinosad increased by a maximum of 30 times, and the yield of avermectin increased by a maximum of 5.8 times.

The invention provides a method for locking a molecular marker related to sheep tail fat weight based on multiple omics strategies and application, and the method is obtained based on whole-genome selection signal analysis of sheep with different tail types, whole-genome association analysis of tail fat weight, epimics sequencing of tail adipose tissue and comprehensive analysis of transcriptome sequencing results. The invention relates to a sheep tail fat weight mutation site, which is located at a chromosome 51760995 of a sheep reference genome Orambouilletv1.0 version sequence 13, and A / C base mutation exists on the site and is obviously related to the sheep tail fat weight. Through amplification of a genome area where the SNP site is located, statistics verifies that the tail fat weight and the tail fat relative weight of an AA genotype individual are significantly lower than those of a CC type individual. By detecting the genotype of the molecular marker, AA homozygous sheep can be selected to enter a core group for breeding, so that the tail fat deposition is reduced, and the economic benefit of the sheep breeding industry is improved.

The application discloses a gene selection method based on biological character expression of multi-target feature selection, which comprises the following steps: step 1, inputting biological gene samples; step 2, grouping features of the input gene samples and generating a sub-population for each group; step 3, independently evolving each sub-population; step 4, gradually merging all the sub-populations; and step 5, finally merging all the sub-populations into one population, searching on the original genes, finally obtaining the best partial genes on the original gene set in expression effect, and taking the partial genes as the finally selected genes. The application can screen a small number of important genes from a large number of gene features, thereby effectively improving the accuracy of gene selection of high-dimensional biological character expression, and is also beneficial to shortening a research period and reducing research cost.

The present invention discloses a multi-MNP molecular marker combination for the body height trait of hybrid pigs and its application, which relates to the field of biotechnology. The multi-MNP molecular marker combination is composed of molecular markers numbered MNP-1, MNP-2, and MNP-3, and the nucleotide sequences of the molecular markers are respectively shown as the nucleotide sequences of SEQ NO.1 to SEQ NO.3 in the sequence listing. The present invention proposes an MNP molecular marker combination for the body height trait of binary hybrid pigs and designs specific primers that can be used for the early judgment of the body height trait of hybrid pigs. It can achieve precise gene selection, thereby efficiently cultivating hybrid pigs with an ideal body shape, realizing rapid and accurate genetic selection in population improvement, accelerating genetic progress, and thus improving economic benefits. It is expected to provide a new tool for the early judgment and evaluation of the body height trait.

The present invention provides a storage medium storing a gene selection method program for gene feature selection. It includes obtaining a training set and a test set from a gene data microarray data set and determining an initial population; performing binary encoding on each individual of the current population using a transformation function; calculating the fitness value of the current population and updating relevant parameters in the salp swarm and moth-flame algorithms; setting relevant parameters of the sine-cosine optimization algorithm and updating the population using the sine-cosine optimization algorithm iteration formula; updating the population obtained by the sine-cosine optimization algorithm successively through the salp swarm, moth-flame and opposition-based learning strategies to obtain three populations; selecting the next-generation population through greedy selection; if the maximum number of iterations is reached, end the loop and output the optimal solution, otherwise continue to iterate until the iterative calculation ends. The present invention can more accurately and efficiently screen out the gene features that contribute the most to the category from genes, reducing the detection cost.

The present invention provides a method for in vitro expression and purification of a thiolquinone oxidoreductase derived from Pseudomonas, and its application to the degradation of propanethiol. This method uses the whole genome of Pseudomonas putida S-1, which can efficiently degrade propanethiol, as a template to clone the thiolquinone oxidoreductase encoding gene sqr. pET28a is selected as the expression vector, and the sqr gene and the linearized vector are used to construct the recombinant plasmid pET28a-sqr by one-step cloning. The recombinant plasmid is introduced into Escherichia coli BL21 by heat shock transformation, and the BL21 / pET28a-sqr expression strain is successfully constructed. After culturing and induction, the SQR protein is purified by gravity method with a histidine tag after ultrasonic disruption. Subsequently, BL21 / pET28a is used as a control strain, and the ability of the recombinant expression strain and the SQR protein to transform propanethiol is determined based on gas chromatography-mass spectrometry (GC-MS). Obtaining a thiolquinone oxidoreductase and characterizing its function of transforming propanethiol by the method provided by the present invention will be beneficial to the application and transformation of thiol-degrading bacteria.

The invention relates to the technical field of bacterial detection, and discloses a kit for detecting diarrheogenic Escherichia coli in a typing manner and application of the kit. According to the kit disclosed by the invention, by adopting a specific gene selection arrangement mode and matching with a specific primer probe sequence, the typing detection of five DEC can be realized only by two tubes and three channels, the result interpretation is relatively simple, the types of required fluorophores are relatively few, the detection quantity is low, and the kit is easier to deal with the screening of large-batch samples of foodborne disease outbreak events; moreover, in cooperation with specific target gene arrangement modes in the tube A and the tube B, detection primers and probe sequences of all target genes are specially designed, synchronous detection of genes of lt, stp, sth, ipaH and escV can be achieved in the tube A, synchronous detection of genes of stx1, stx2, aggR, astA and pic can be achieved in the tube B, and high detection specificity, sensitivity and accuracy are achieved.

A method of training an artificial neural network to predict gene expression for a patient having a pathological condition is provided. The method comprises obtaining a set of histopathological images from a database of patients having the pathological condition, collecting from the database a set of gene expression profiles corresponding to the histopathological images, identifying in the set of gene expression profiles a set of considered genes, selecting a training subset of the considered genes comprising genes characterized by similar median gene expression values, and training the neural network to predict a gene expression value of an input histopathological image, using gene expression profiles of the genes in the training subset simultaneously.

This invention discloses a gene selection system based on homomorphic encryption technology, comprising: an input module for inputting data; a selection module for allocating the input data to corresponding prediction models and obtaining all information from the data and pedigree files in a one-step process; an execution prediction module for reconstructing the computation array according to the configuration information provided by the selection module to implement the algorithms for each stage of homomorphic encryption; a display module for storing the initial plaintext, intermediate ciphertext, and plaintext obtained after decryption of the homomorphic encryption algorithm in the execution prediction module; and a cloud output module for outputting the encrypted data to a cloud platform. This invention also develops a local client, HEcryptor, based on the homomorphic encryption algorithm to work with the HEGS platform. Users can encrypt data locally before uploading to ensure complete data security. The encrypted data can be used for both single-field and cross-field genome selection, meeting various user needs.

The application belongs to the technical field of biology and relates to the prevention and treatment of Epilachna vigintioctopunctata, in particular to a dsRNA of an insect neuropeptide ecdysis-triggering hormone ETH gene and a preparation method and application thereof. The neuropeptide ETH gene of Epilachna vigintioctopunctata is screened through transcriptome sequencing; a fragment of the gene sequence with SEQ ID No. 2 is selected to synthesize dsRNA (dsETH). After 3-day-old larvae of Epilachna vigintioctopunctata are fed with dsETH (concentration >= 1 ng / μL), more than 70% of the Epilachna vigintioctopunctata are deformed and cannot feed on the 7th day, more than 70% of the Epilachna vigintioctopunctata die on the 10th day, and the deformation rates of 5, 10, 25 and 100 ng / μL are 85%, 90%, 95% and 100% respectively. The application provides a new prevention and treatment target for the prevention and treatment of Epilachna vigintioctopunctata based on RNA interference.