15 results about "Gene selection" patented technology

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 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 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 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 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 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.

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.