84 results about "Gene selection" patented technology

This invention encompasses compounds, structures, compositions and methods for therapeutic guide molecules that direct CRISPR gene editing. A guide molecule for directing gene editing can be allele selective, or disease allele selective, and can exhibit reduced off target activity. A guide molecule can be composed of monomers, including UNA monomers, nucleic acid monomers, and modified nucleotides, wherein the compound is targeted to a genomic DNA. The guide molecules of this invention can be used as active ingredients for editing or disrupting a gene in vitro, ex vivo, or in vivo.

The invention discloses a selection method for subintervals of near infrared spectral characteristics based on a simulated annealing-genetic algorithm. The method comprises the following steps: pretreating a near infrared spectrum; then dynamically dividing subintervals on the pretreated near infrared spectrum, introducing an Metropolis criterion in the simulated annealing algorithm to gene exchange and gene selection operators, and selecting an optimal character subinterval with the simulated annealing-genetic algorithm; and finally judging the best subinterval division method to be combined with the optimal character subinterval and building a PLS model for the selected optimal character subinterval. In the selection method, high-quality offspring individuals can be generated through improved variation and commutating operators, not only adaptability levels of overall populations are improved, but also enough power for population evolution is provided; and deficiency brought by the total number of the spectrum subintervals manually designated according to the experiences in the process of modeling can be avoided, and spectral models with high precision and strong prediction ability can be rapidly obtained.

The invention discloses a new cell type identification method based on similarity learning and enhancement thereof. The method designs a new global similarity calculation method, combines with other three conventional local similarity information, screens the genes, and carries out enhancement processing on the global similarity with sparse properties. According to the method, a global similaritycalculation method different from the traditional calculation of the local point-to-point similarity is used, the gene selection and the similarity enhancement are carried out by combining multiple different similarities including the global similarity and the local similarity, and a similarity matrix rich in information is obtained. According to the method, the influence of the factors, such as the technical noise, the biological noise, etc., carried by the single cell data can be effectively reduced, and the type of the single cell can be more accurately identified.

The invention relates to a tea tree miRNA fluorescent quantitative PCR reference gene Pc-222-3p, which has a nucleotide sequence as shown in SEQ ID NO: 1, as well as a screening method of the reference gene. According to the method, 6 common plant miRNA reference genes and 3 tea tree miRNAs are selected as candidate reference genes, and the expression stabilities of the candidate reference genes under different low temperature stresses and in different tea tree tissues are evaluated by virtue of qRT-PCR technology through two software, namely BestKeeper and geNorm, so as to provide a reference for the selection of reference genes for tea tree miRNA differential expression analysis under low temperature stress. The obtained tea tree miRNA fluorescent quantitative PCR reference gene Pc-222-3p is stable in gene expression under different temperatures of treatment and in different tea tree tissues; therefore, the reference gene Pc-222-3p can be used as a reference gene for tea tree miRNA fluorescent quantitative PCR under low temperature stress.

An apparatus 11 for estimating internal biological time of a biological individual on the basis of quantity data of a gene product as measured in a specimen sampled from the individual. The apparatus includes circadian oscillatory gene selection means for selecting circadian oscillatory genes whose time-course change in gene product quantity data as measured in standard specimens approximates a cosine curve having a predetermined period; circadian expression curve selection means for selecting, from among a plurality of cosine curves having different phases and sharing a specific period, a circadian expression curve which is similar to the pattern of time-course change in the expression product quantity of each of the above-selected circadian oscillatory genes; and registration means for registering information which identifies the selected circadian expression curve.

The invention relates to a method for screening anthracycline cardiotoxicity genes through mRNA expression profiles and competitive endogenesis RNA expression profiles jointly. The method includes the steps that expression values of a large quantity of genes are detected through mRNA and competitive endogenesis RNA expression profile chips, and the correlations of all the genes are ordered; the obtained mRNA feature genes and the obtained competitive endogenesis RNA feature genes are combined; a gene pool is subjected to further gene selection with a genetic algorithm; differential expression genes are selected by comparing the data between different samples of mRNA and competitive endogenesis RNA joint expression profiles of breast cancer patients, breast cancer AIC patients and able-bodied persons; the differential expression genes are predicted, and the genes with the scores larger than 140 and free energy smaller than 20 are selected as reliable target genes; genes with obvious differences are selected from the target genes, and constructed breast-cancer anthracycline cardiotoxicity attacking target genes are verified through the residual RNA. By means of the method, more reliable potential gene markers related to anthracycline cardiotoxicity can be selected as a candidate.

The invention discloses a peanut high-oleic-acid character gene selection method. The method comprises the steps of extracting leaf gene groups of different peanut breeds, and amplifying AhFAD2A and AhFAD2B genes from the gene groups by using a specificity primer; cutting and recycling specificity amplification stripes and sequencing to obtain the sequence of the gene; translating the obtained sequence, comparing and analyzing, and searching the point mutation or inserting site to judge the oleic acid content and specific value of oleic acid/linoleic acid (O/L) of the breed. The method is simple to operate, variety of peanuts germ plasm can be analyzed, the oleic acid content of single plant of peanut can be determined in the seedling stage, the breeding process can be shortened and the breeding effect of oleic acid can be improved by combining comprehensive agronomic character selection of the single plant.

The invention discloses a cancer gene expression profile data identification method based on integration of extreme learning machines. The method includes the steps of selection and integration of member extreme learning machines (ELMs). The method concretely includes the steps that preprocessing is carried out on a cancer gene expression profile data set, wherein the preprocessing includes gene selection and normalization of expression profile data; N sample sets are generated through a Bagging method, and each sample set is divided into a training set and a verification set according to a certain proportion; N ELMs are generated on the N training sets in a learning mode, and L ELMs (L&1t; N) with the highest recognition rate on the corresponding verification sets are selected to form an alternative member ELM base; K member ELMs (K&1t; L) forming an integrated system are selected from L ELMs based on the particle swarm optimization algorithm; the integrated vote weight of K member ELMs is worked out by utilizing the minimum-norm least square method; an integrated ELM system is obtained, and the integrated ELM system is used for performing tumor recognition on a newly increased cancer gene expression profile sample. Through the method, the cancer gene expression profile data can be quickly and accurately recognized.

The invention discloses a tumor feature gene selection method combining mRNA and microRNA expression profile chips. The method is implemented by the following steps of step 1, by using the mRNA and microRNA expression profile chips, detecting expression values of a large amount of genes, by adopting a filtration feature gene selection method, ranking relevance of all the genes, removing a large amount of low-relevance genes, and leaving a small amount of genes that are closely related to tumor classification; step 2, combining mRNA and microRNA feature genes obtained by adopting the filtration feature gene selection method to form a gene pool U; and step 3, by adopting a genetic algorithm, further selecting genes for the gene pool, eliminating redundant genes, and performing a search to obtain an optimal gene set S with optimal features. The method is simple in step and high in result accuracy.

The invention relates to lactic acid galactococcus food-grade secretion expression vector, and the preparation method and the application thereof, in particular to the lactic acid galactococcus food-grade secretion expression vector which comprises the following components: a thyA gene selection marker, replicons of plasmid pWV01, multiple cloning sites, promoter used for secretion expression, a ribosome bind site of Usp45, a signal peptide sequence of Usp45 and a partial Usp45 mature peptides coded sequence.

The embodiment of the invention discloses a gene data processing method and a gene data processing device. The method comprises the following steps: receiving gene data of a specified feature type of a reference population; preprocessing the gene data to obtain standardized gene data; performing feature gene selection on the standardized gene data by a LASSO method to obtain feature gene data; upon a cross validation method, dividing a sample set of the feature gene data into test samples and training samples; injecting the training samples into a classifier to obtain a trained classifier; injecting the test samples into the trained classifier; performing feature classification on the test samples, and performing statistics on the classification accuracy of the classifier. According to the gene data processing method and the gene data processing device provided by the embodiment of the invention, the accuracy of the feature gene selection can be improved, and the influence of the selection of the test samples and the training samples on the classification accuracy is lowered.

The invention discloses a novel feature gene selection method based on logistic and relevant information entropy. The method comprises the following steps that a dataset is subjected to logistic regression, a gene variable with great influence on the classification is obtained, a Relief algorithm is used for giving a value on the gene variable, sequencing is carried out, a maximum feature value gene is added to an initial feature gene set, and the relevant information entropy is calculated. The novel feature gene selection method has the advantages that a logistic regression model in the machine study is introduced into the feature gene selection method, and a high-quality gene expression profile is obtained; the correlation between gene variables is measured by the relevant information entropy, redundant genes are deleted, and a feature gene sub set with high classification capability and fewer genes is obtained through searching a feature gene space set.

The invention provides a breeding method of dedicated silkworm hybrid secreting natural and green sericin with high content, comprising the following steps: taking exarate pupae breeds (with Nd-s genes) as stuff to be hybridized with the breeds with high amount of silk production, and breeding Japanese special original strains with sericin content being more than 99.0% and homozygous genotypes (Nd-s/Nd-s) by adopting the methods of selfing, backcrossing, marker gene selecting, sericin content tracking and detecting and the like; and hybridizing the Japanese special original strains with the Chinese conventional breeds (Gc/Gc) with green cocoon properties to breed the dedicated silkworm hybrid. The method can provide high quality raw materials to such fields as cosmetics, medicines, food, fiber modified materials and the like and improve the product added value.

The invention relates to a heuristic breadth-first searching method for cancer-related genes. According to the method, appearance frequencies of genes in a selected gene subset are used for measuring the genes, and genes with higher appearance frequency are considered as the most important cancer-related genes, on the basis, a classifier is designed and a gene ordering method based on HBSA is established. As proved by study, information gene selection plays an important role in improving the classification performance, and the genes can be probably taken as important tumor clinical diagnosis signs, so discovery of the minimum gene subset with the highest classification performance is a very important research objective. As indicated by experimental results, the heuristic breadth-first searching method can not only obtain favorable generalization performance but also discover important tumor genes. And the relationship of the appearance frequencies of the selected genes and the gene number conforms to power-law distribution. The genes in the gene subset with extremely high classification accuracy are in close relationship with specific tumor subtypes, and even the genes are important genes directly related with the tumor.

The invention discloses an industrial robot trajectory optimization method based on an improved genetic algorithm, and aims to provide a trajectory through which an industrial robot completes a given route with the shortest time and the minimum impact based on the given path and a certain constraint condition. Based on a traditional genetic algorithm, the industrial robot trajectory optimization method disclosed by the invention improves a gene selection formula, a crossover formula and a mutation formula, improves crossover and mutation probabilities on the basis of self-adaption PRGA, optimizes the action time of a mechanical arm under the condition that the motion trail of the robot is stable, reduces resonance and shaking, caused by overlarge impact, of the robot, and enables the robot to run stably and smoothly, thereby greatly improving the working efficiency and prolonging the service life of the robot.

A feature gene search method based on deep learning and evolutionary computation includes the following steps: 1) calculating a differential expression level index and establishing a primary gene poolaccording to a Pareto principle; 2) according to the expression level of each gene, calculating a density matrix and a distance matrix after the mapping of the genes, drawing a decision map, using multiple linear regression analysis to fit a binary plane, and automatically determining a clustering center; 3) constructing a deep gene expression prediction network to calculate gene-genetic susceptibility information (GGSI) of a primary gene pool; 4) removing redundant genes based on GGSI values and creating a secondary gene pool; 5) performing binary coding on a cuckoo search algorithm based onthe GGSI values, selecting the most compact gene sets, and establishing a tertiary gene pool. According to the method, a feature gene selection framework is provided based on a hierarchical structure, which can extract key genes better and use multiple linear regression analysis combined with a deep learning algorithm and an optimization algorithm to select the most compact feature gene sets.

A method for realizing orthogonal mirror image filter set based on genetic algorithm includes generating multi-set of random number to represent parameter of orthogonal mirror image filter set, coding each set of random number to binary system character string in finite length according to current gene selection probability, decoding each character string and calculating out adaptive degree, updating said probability according to gene of character string with highest adaptive degree, repeating said step till ending condition is satisfied, obtaining parameter of said filter set by decoding character string with highest adaptive degree and utilizing obtained parameter to realize said filter set.

In a gene expression profile retrieving apparatus, while gene expression profile data acquired with different platforms can be used, a cell can be retrieved based upon a gene expression profile. The gene expression profile retrieving apparatus is provided with: a gene expression profile DB stores gene expression profiles of known cells in a plurality of different platforms; a reference gene selecting unit operable to select a plurality of reference genes from genes which are commonly contained in both an inquiry profile and a platform of a gene expression profile stored in the gene expression profile DB; an order applying unit operable to apply orders to the inquiry profile and the reference gene of the known cell stored in the gene expression profile DB according to the expression level; and an analogous cell determining unit operable to acquire from the gene expression profile DB, such a cell which is analogous to the gene expression profile of the inquiry profile in the highest degree based upon the applied order.

The present application relates to a system for designing promoters for selective expression of genes. Thereby identified transcription regulatory elements are selected according to a specific methodology and used to create a library of transcription regulatory elements, which are then used to construct specific promoters, especially tissue-specific promoters.

The invention provides a gene selection method which is used for gene characteristic selection. The method comprises the steps of obtaining a training set and a test set through a gene data microarraydata set, and determining an initialized population; carrying out binary coding on each individual of the current population by adopting a conversion function; calculating the fitness value of the current population, and updating related parameters in the dolio-sea squirt and moth fire suppression strategy; setting related parameters of a sine and cosine optimization algorithm, and updating the population by adopting a sine and cosine optimization algorithm iterative formula; updating the populations obtained through the sine and cosine optimization algorithm through a doliola scabra, moth fire suppression and reverse learning strategy in sequence so as to obtain three populations; selecting a next generation of population through greedy selection; and if the maximum number of iterationsis reached, ending the loop and outputting the optimal solution, otherwise, continuing the iteration until the iterative computation is ended. According to the invention, the gene characteristics which contribute most to categories can be screened out from the genes more accurately and more efficiently, and the detection cost is reduced.

The invention belongs to the field of crop genetic breeding, and discloses a molecular marker for raphanus sativus L. downy mildew resistant gene close linkage. A sequence-related amplified polymorphism (SRAP) molecular marker method is used for carrying out marker genotyping on high-resistance downy mildew raphanus sativus L. parents (NAU-dhp08), high-sensitive downy mildew raphanus sativus L. parents (half green from autumn fields) and a second filial generation (F2) group single plant which is prepared through resistance and sensitive parent hybridization, through the F2 single plant resistance phenotype and marker gene linkage analysis, a resistance marker Em9/ga24370 of 370-base pair (bp) close linkage is identified, a distance from a resistance locus and a marker genetic distance is 2.3 centimeters. The molecular marker method provided by the invention can be used for the marker assisted selection of raphanus sativus L. downy mildew resistance breeding, quickly and accurately identifies the raphanus sativus L. downy mildew resistance, overcomes the limitations of heavy workload, long period, easily influenced results by environment and the like of the conventional resistance identification method, the high-resistance gene selection efficiency can be obviously improved, so that a breeding process of a new high raphanus sativus L. downy mildew-resistant variety can be accelerated.

The invention discloses a feature gene selecting and cancer classifying method which at least comprises the following steps of establishing a logistic regression model according to a super-parameter set and a to-be-processed gene data set; according to maximum likelihood estimation and logarithmic operation, expressing the logistic regression model as a loss function; establishing an SCAD-Net resolving model; according to the loss function and the SCAD-Net resolving model, obtaining an SNL model; calculating an iteration updating operator of the SCAD-Net; according to the iteration updating operator, calculating a gene regression coefficient of the SNL model through a coordinate gradient descent method; and according to the gene regression coefficient, performing feature gene selection andcancer classification. The feature gene selecting and cancer classifying method can effectively improve feature gene selecting and cancer classifying accuracy, thereby facilitating disease researching.

The invention relates to a PCA3 and PSA RNA detection kit and amplification system. The detection kit includes primers and fluorescent probes aiming at PCA3 and PSA genes, and a Stoffel fragment and Tfl DNA polymerase; the primers aiming at the PCA3 gene are shown as SEQ ID NO. 1 and SEQ ID NO. 2, and the sequence of the fluorescent probe is shown as SEQ ID NO. 3; and the primers aiming at the PSAgene are shown as SEQ ID NO. 10 and SEQ ID NO. 11, and the sequence of the fluorescent probe is shown as SEQ ID NO. 12. Based on existing kits, the detection and amplification system of real-time fluorescence quantitative RT-PCR can be optimized; the PCA3 gene selects the primers and probes which have better detection effects and are designed by the sequence on exon 3; optimized Tris-MOPS-sodiumcitrate buffer which is more suitable for RNA amplification can be introduced into a reaction system, and the Stoffel fragment or the Tfl DNA polymerase is introduced as well; and therefore, the kit can tolerate more templates while detection sensitivity and specificity are enhanced.