2328 results about "Phenotype" patented technology

The invention relates to the technical field of data decision, in particular to an analysis decision method for crop growth monitoring data. The method comprises the following steps: collecting crop phenotypic characteristic data through a multi-source sensing array; dynamically dividing crop growth response stages according to phenotypic characteristics; constructing a growth situation prediction model based on the crop growth response stage; inputting the phenotypic characteristic data into a growth situation prediction model, simulating a crop growth process through a digital twin engine, and generating growth situation evolution data; extracting physiological anomaly characteristics of the growth situation evolution data, and generating stress response signals; obtaining a canopy three-dimensional phenotype map based on the stress response signal; positioning vascular bundle nodes of the canopy three-dimensional phenotype map through morphological topology analysis; according to the invention, through multi-source data acquisition, dynamic growth stage division, accurate model prediction, three-dimensional phenotype monitoring and self-correction decision optimization, the accuracy of analysis decision for crop growth monitoring data is improved.

The invention relates to the field of gene editing, in particular to a peony PoLEC1 gene and application of protein coded by the peony PoLEC1 gene. The peony PoLEC1 gene is cloned from cultivated peony paeonia ostii, after the peony PoLEC1 gene is transformed into arabidopsis thaliana, phenotypic contrast finds that the peony PoLEC1 gene can play a role in bolting and flowering as well as growth and development of plants, specifically, overexpression of the peony PoLEC1 gene can increase the number of leaves, delay bolting and flowering time and reduce the height of the plants. According to the invention, arabidopsis thaliana is taken as a model plant, and a certain theoretical basis is provided for establishing a complete plant regeneration and genetic transformation system for peony by exploring the functional characteristics of the peony PoLEC1 gene and the expression characteristics of the peony PoLEC1 gene in the whole somatic embryo direct generation process and in each tissue of overexpressed arabidopsis thaliana.

The invention discloses a pennisetum purpureum gene PpbHLH148L and application thereof in improving the cold resistance of plants, and relates to the technical field of gene engineering. The nucleotide sequence of the gene is as shown in SEQ ID NO.5. After the gene is subjected to subcellular localization and expression condition analysis after cold stress, the gene is related to low-temperature tolerance. An overexpression vector of the gene is constructed, Agrobacterium tumefaciens is transferred, then Arabidopsis thaliana infection is carried out, cold tolerance determination is carried out on overexpressed Arabidopsis thaliana, phenotypic difference and physiological index difference between an overexpressed strain after cold treatment and a wild type are observed, and it is found that the gene can significantly improve the low-temperature stress tolerance of Arabidopsis thaliana. And in the low-temperature treatment of the pennisetum alopecuroides transgenic callus, the transgenic callus also shows higher tolerance than the wild callus at low temperature. The pennisetum purpureum gene PpbHLH148L can be used for low-temperature tolerance modification of plants, and has an application prospect in creation of low-temperature-resistant plant materials.

The invention provides a germination form measurement method and system based on improved U-Net to realize automatic measurement, reduce labor cost and significantly improve efficiency, thereby optimizing tomato planting and management. According to the application, the seed germination form acquisition system is used for acquiring images of seed culture and seed germination processes, a data set is constructed, and the data set is used for training the improved U-Net model. According to the invention, the up-sampling of the model is replaced by the DBB module, and the CAA is introduced into the jump joint and the decoder. The MIOU index and the MPA index of the model can be respectively improved by 2.03% and 0.39% compared with those of the basic U-Net. According to the method, based on the segmentation result of the improved model, the radicle length can be extracted in combination with the canny algorithm. Compared with the ImageJ measurement result, the correlation coefficient of the model can reach 0.991, and the measurement efficiency is improved by 300 times. According to the method, the influence of drought, salinization and different molds on tomato radicle germination can be evaluated, and an efficient solution is provided for nondestructive monitoring and crop management in precision agriculture.

The invention relates to the field of bioinformatics and machine learning, in particular to a rice genotype and phenotype association prediction method based on machine learning. The method comprises the following steps: collecting genotype data and phenotype data of a rice sample, and generating genotype features by adopting a dynamic feature extraction algorithm so as to obtain a genotype feature set; and S2, constructing a genotype and phenotype association prediction model, and predicting phenotypes based on the genotype feature set. The problems that feature information of genotype data is difficult to accurately capture due to non-uniform data distribution or periodic change of different gene loci, and complex features are difficult to recognize by an existing feature extraction method are solved; in the aspect of agricultural genotype and phenotype prediction, especially in the fields of rice breeding, disease resistance enhancement and yield optimization, an effective method special for bioinformatics feature extraction and correlation prediction is lacked; the technical problem of excessive dependence on some unimportant or redundant features due to insufficient performance during feature redundancy processing is solved.

The invention belongs to the technical field of agricultural information, and discloses a rape variety environmental adaptability evaluation system based on machine learning. The system is composed of a data acquisition standardization unit, a regional environment feature modeling unit, a variety phenotype and pedigree association unit, a feature screening and model training unit, a regional adaptability scoring and decision-making unit, a dynamic learning and evolution updating unit and an evaluation visualization and variety recommendation unit. By performing standardization processing on multi-source data, format and dimension differences are eliminated, and a high-quality data foundation is laid for subsequent analysis. Fusing environment and variety bilateral characteristics, and enabling the model to capture environment influence and variety heredity characteristics at the same time. Advanced methods such as ensemble learning and a time sequence neural network are adopted, and the complex relation is accurately mined. Compared with a traditional field test and the prior art, errors are greatly reduced, the adaptation degree of the variety in different areas is more accurately judged, a scientific basis is provided for planting recommendation, and the yield and quality of the oilseed rape are improved.

The invention discloses a method for predicting images in a whole growth period based on crop genotypes of artificial intelligence. The method comprises the steps of collecting crop images, processing the crop images, constructing a multi-layer perceptron neural network and a condition control generative adversarial network for training so as to learn the mapping relation of crop genotypes, phenotypes and images in the whole growth period, obtaining a crop gene-phenotype prediction model and a phenotype-image prediction model, and finally obtaining the crop genotype-phenotype prediction model and the phenotype-image prediction model through the two models. Based on the genotype of a to-be-predicted crop, a visual result of the whole growth period of the crop in an ideal environment under the regulation of the gene is predicted. According to the method, based on an artificial intelligence technology, a multi-dimensional space multi-layer perception network and a condition control generative adversarial network are constructed, and vivid and accurate visual prediction of crops is realized by deeply utilizing mapping rules contained in crop genes, phenotypes and image big data.

The invention discloses an upland cotton wild germline salt-tolerant gene GhSHOU4 and application thereof, the gene ID of the cotton gene GhSHOU4 is GHA05G1741, and the nucleotide sequence of the cotton gene GhSHOU4 is as shown in the sequence 1. According to the invention, the wild germline gene GhSHOU4 of the upland cotton is cloned from the wild germline of the upland cotton, and a subcellular localization result shows that the GhSHOU4 protein is localized on a cytoplasmic membrane. Gene silencing cotton is obtained by using a VIGS technology, phenotype identification and physiological and biochemical index determination after salt stress treatment show that the malondialdehyde content of a gene silencing plant is significantly increased, and it is proved that the tolerance of the cotton after silencing the gene under salt stress is significantly reduced. Good gene resources are provided for salt-tolerant molecular breeding and genetic improvement of cotton.

Systems and methods for predicting a medical trait in an individual are provided. The techniques include obtaining genomic data including information indicative of genetic variants present in a genome of the individual and calculating, using an at least one processor and a trained computational model, a trait score based on the information indicative of the genetic variants, wherein the trained computational model was obtained by training a first computational model using data describing phenotypes, genotypes, and / or phenotype-genotype relationships for a population including admixed individuals of multiple ancestries. The trait score may be used to alter a course of medical treatment of the individual and / or to implement a clinical trial.

Embodiments are directed to a method for determining relationships between genes, phenotypes, and diseases that includes extracting a first set of biological data containing gene data, phenotype data, and disease data from a database, extracting a second set of biological data from a database, creating nodes on a network pertaining to each individual gene, phenotype, and disease data extracted from one of a database and documents, training multiple machine learning (ML) algorithms on a set of extracted data with matching empirical results, using at least one of the ML algorithms and the second set of biological data for determining relationships between the nodes, creating at least one of a gene-disease association score, gene-phenotype association score, and disease-phenotype association score for the relationships based on the relative association of the nodes, and displaying at least one of the association scores to a user.

The invention relates to the technical field of bioinformatics, and provides a crop whole genome phenotype prediction method and system fused with an environmental indicator gene, and the method comprises the following steps: collecting re-sequencing data, and carrying out genome comparison to obtain variation site data; performing whole genome association analysis by using the variation site data to obtain phenotype association site information; carrying out gene expression quantity measurement on samples of the crop population material in different environments to obtain gene expression quantity data; performing differential expression analysis on the gene expression quantity data to screen environmental indicator genes to obtain an environmental indicator gene set; constructing a phenotype prediction model of double-branch fusion; and predicting a to-be-predicted material through the phenotype prediction model to obtain phenotype prediction results for different environments. According to the method, environmental factors are incorporated into the whole genome selection model, so that the phenotype prediction precision in different environments is improved.

The invention discloses a method for targeted creation of a fast-growing new tilapia strain, and belongs to the technical field of fish cultivation. The method specifically comprises the following steps: (1) taking a fragment, as shown in SEQ ID NO.1, on a third exon of a pomc gene as a gRNA target sequence; (2) designing and synthesizing a gRNA primer sequence; (3) amplifying by adopting the gRNA primer sequence and the gRNA plasmid, and then transcribing and purifying to obtain a gRNA fragment; (4) transferring the gRNA fragments into fertilized eggs of tilapia mossambica through gene editing to obtain F0-generation positive fish; and (5) hybridizing the F0-generation positive male fish with the wild-type female fish, screening out F1-generation parent fish, and culturing the F1-generation parent fish to obtain F2-generation tilapia with homozygous deletion mutation. According to the invention, the pomc homozygous knockout line is established in the Nile tilapia mossambica for the first time, the new fast-growing line is a complete gene function deletion model, the cost is lower, the efficiency is higher, and the phenotype can be stably inherited.

The present invention relates to the technical field of molecular markers for maize resistance to stalk rot and genetic breeding, and in particular, to a QTL related to maize resistance to stalk rot, SNP molecular markers closely linked with the QTL, and a use of the SNP molecular markers. Provided is a QTL related to maize resistance to stalk rot, wherein the QTL is located on maize chromosome 10. Also provided are SNP molecular markers Ks4 and Ks6 closely linked with a QTL site for maize resistance to stalk rot. By detecting the SNP molecular markers, the genotype of a maize material under test can be accurately identified in a high-throughput manner, a material resistant to stalk rot is selected, and phenotype identification and assisted screening of the maize resistance to stalk rot can also be carried out in a seedling stage, thus significantly reducing production cost and significantly enhancing the efficiency of breeding maize resistant to stalk rot.

The invention discloses an OsFBX421 gene and application of the OsFBX421 gene in the aspect of improving the salt tolerance of rice. The nucleotide sequence of the OsFBX421 gene is as shown in SEQ ID NO: 1 in a sequence table. The invention further discloses a phenotype of an OsFBX421 gene mutant strain under treatment of 120 mM NaCl, and the protein and the coding gene thereof have important theoretical and practical significance on research of a plant salt-tolerant mechanism and improvement of salt tolerance and related traits of plants, can play an important role in plant salt-tolerant molecular breeding and have wide application prospects.

The invention belongs to the technical field of crop genetic breeding, and relates to a linked molecular marker of a peanut hypocotyl elongation related gene locus, an identification method and application of the linked molecular marker. The primer pair is C2-15-F / R, and the sequence of the primer pair is as shown in SEQ ID NO. 1 and SEQ ID NO. 2; by designing an InDel marker C2-15, the peanut hypocotyl length character can be quickly distinguished by using a primer to amplify a target band and using polyacrylamide gel electrophoresis detection. The identification method is more accurate and convenient, has good repeatability, can efficiently distinguish the phenotype and genotype of an individual, shortens the experiment process, has the advantages of simple and feasible operation steps and the like, and saves expenditure. The method disclosed by the invention has important application value in the breeding work aspects such as peanut hypocotyl elongation character related gene positioning, genotype typing and later cultivation of excellent peanut varieties.

The invention discloses a plant salt tolerance grade evaluation method based on three-dimensional point cloud. The method comprises the following steps: obtaining three-dimensional point cloud data of a plant to be detected; processing the three-dimensional point cloud data of the plant to be detected, and removing noise and abnormal points in the data; inputting the three-dimensional point cloud data into a learning model to obtain three-dimensional point cloud data of each leaf of the plant to be detected; the salt tolerance coefficient of the three-dimensional point cloud data of each leaf is obtained, and the salt tolerance coefficient of the plant is obtained; carrying out salt tolerance evaluation and grading; the fresh weight of each plant is measured, and photosynthetic phenotype parameters of the plants are measured through a photosynthetic phenotype imaging system; and verifying and optimizing. According to the plant salt tolerance grade evaluation method based on the three-dimensional point cloud, the limitation of an existing plant salt tolerance evaluation method is overcome, salt tolerance key characteristics are extracted through the data dimension reduction technology to establish a salt tolerance evaluation membership function, plant salt tolerance quantification and automatic grade division are achieved, and the plant salt tolerance evaluation efficiency is improved. And a scientific basis is provided for screening salt-resistant varieties.

The invention discloses an intelligent computing platform for animal gene breeding, which adopts RFID electronic ear tags or other biological recognition technologies to carry out individual marking and identity authentication on each breeding pig, collects basic information of the breeding pig in real time, and carries out intelligent computing on the basis of a statistical genetics model in combination with a machine learning algorithm. The method comprises the following steps: collecting phenotypic data and genotypic data, carrying out deep analysis on the collected phenotypic data and genotypic data, calculating a breeding value or a genome breeding value of each breeding pig, designing and applying an optimization algorithm according to a genetic evaluation result, formulating an optimal breeding pig mating plan, and realizing automatic monitoring of pig house environment parameters through an Internet of Things technology. The method comprises the steps of performing intelligent analysis on behaviors and postures of pigs in combination with an AI image recognition technology, developing a matched software system, integrating data analysis results, establishing a database based on a cloud computing architecture, constructing a visual platform containing various charts and reports, and converting complex data into visual and understandable information.

The invention relates to the technical field of Chinese trumpet creeper breeding, and discloses a Chinese trumpet creeper-based efficient breeding decision support system and method. The method comprises the following steps: acquiring multi-dimensional phenotypic data in a Chinese trumpet creeper breeding test, wherein the multi-dimensional phenotypic data comprises plant height, leaf area, flowering period and yield indexes; performing normalization processing on the multi-dimensional phenotypic data, eliminating influences of different dimensions, and calculating a variable coefficient of each phenotypic index; then screening out phenotypic indexes of which the stability is higher than a preset threshold value based on the variable coefficient, and constructing a Chinese trumpet creeper phenotypic feature matrix; carrying out dimensionality reduction on the Chinese trumpet creeper phenotypic feature matrix by adopting an adaptive weighting algorithm, and extracting key phenotypic features; inputting the key phenotypic features into a genetic algorithm optimization module, and calculating the contribution degree of each genetic locus in combination with the Chinese trumpet creeper genotype data; and constructing a Chinese trumpet creeper breeding decision model according to the contribution degree of the gene locus, and outputting an optimal breeding combination scheme. The system can optimize the Chinese trumpet creeper breeding process and provide effective support for Chinese trumpet creeper breeding work.

A health and wellness system for a non-human subject comprising analyzing genetic data and phenotypic data of the non-human subject with a machine learning algorithm and making a recommendation or recommendation for products or activities for the non-human subject.

The invention belongs to the technical field of sweet potato breeding, and provides an SNP marker related to sweet potato root color and anthocyanin content and application of the SNP marker, the SNP marker is located at the 25658800th site of a chromosome 10a of sweet potatoes, and the base polymorphism is A / T. When the SNP genotype of the sample to be detected is A / T, the content of anthocyanin in the tuberous roots is high, and the color is purple; when the SNP genotype is T / T, the content of anthocyanin in the tuberous roots is low, and the tuberous roots are non-purple in a large probability. According to the detection method and the kit provided by the invention, the efficiency of identifying the purple sweet potatoes by the developed SNP marker through sweet potato natural population phenotype identification and verification reaches 100%, the efficiency of identifying the non-purple sweet potatoes reaches 85.04%, the color of the sweet potato tuberous roots and the anthocyanin content can be rapidly and accurately detected, and the detection method and the kit have the advantages of being simple and convenient to operate, low in cost and the like, and have wide application prospects. The requirement of large-scale molecular marker-assisted selection can be met.

The invention belongs to the technical field of molecular markers and germplasm resource identification, and particularly relates to an Indel molecular marker for identifying upland cotton drought resistance and application of the Indel molecular marker. The Indel molecular marker is located at the 737th basic group of a nucleotide sequence as shown in SEQ ID NO.1, the genotype of the Indel molecular marker is G in the germplasm of the drought-resistant cotton, and the genotype of the Indel molecular marker is GGATATCAAAATTTTAATCCGTATTATCTAATTGCCTAATAAAAGGA in the germplasm of the sensitive drought cotton. The Indel molecular marker provided by the invention is closely linked with the drought resistance phenotype of upland cotton, can be used for efficiently and accurately identifying or assisting in identifying the drought resistance of to-be-detected cotton and effectively distinguishing drought-resistant and sensitive drought-resistant cotton germplasm, and provides an effective means and way for molecular marker-assisted selection of drought-resistant cotton and cultivation of new cotton germplasm resources.

The invention belongs to the technical field of molecular genetic breeding, and particularly relates to a molecular marker Zm00001eb181850 gene related to corn leukoderma resistance and application thereof, the invention provides a molecular marker related to the corn leukoderma resistance, the gene sequence of the molecular marker is shown as SEQ ID NO: 1 and / or SEQ ID NO: 2, and the molecular marker Zm00001eb181850 gene is shown as SEQ ID NO: 1 and / or SEQ ID NO: 2. Researches prove that corn white spot resistance QTL qMWS4-1 and qMWS4-5 intervals and resistance SNP: 4-118983592 have co-localization, the corn white spot resistance QTL has a high phenotypic interpretation rate, the associated gene Zm00001eb181850 (SEQ ID NO: 2) is highly expressed in corn leaves, it is proved that the associated gene Zm00001eb181850 (SEQ ID NO: 2) is associated with the corn leaf spot, the corn white spot resistance is a quantitative character jointly regulated by multiple genes, and the corn leaf spot resistance QTL has a good application prospect. According to the technical scheme provided by the invention, a new method is provided for cultivating a new variety of leukoderma-resistant corn by utilizing a modern molecular breeding technology.

The invention provides application of a Zm00001eb378820 gene in molecular marker-assisted breeding for regulating and controlling the hundred-grain weight of corn grains, and relates to the technical field of agricultural biology. A backbone maize inbred line Ye107 is used as a male parent and hybridized with three excellent inbred lines (D39, R-2-1-1 and YML1218), a multi-parent group with remarkable hundred-grain weight difference is constructed, and SNP27712538 positioned on a chromosome 9 is a remarkable associated site by measuring hundred-grain weight phenotype data in multiple environments and combining high-density SNP (Single Nucleotide Polymorphism) markers, GWAS analysis and QTL (Quantitative Trait Loci) positioning. And the functional gene Zm00001eb378820 which is closely related to the regulation and control of the corn hundred-grain weight is excavated. A new insight is provided for a genetic regulation mechanism of the corn hundred-grain weight, and a key target spot is provided for breeding of high-yield corn varieties, so that the breeding efficiency is remarkably improved.

Provided is the use of overexpression of the auxin transporter PtoPIN1a gene in increasing the biomass of Populus tomentosa. The nucleotide sequence of the auxin transporter PtoPIN1a gene is as shown in SEQ ID No. 1. By means of using a genetic engineering method, wild-type Populus tomentosa is transformed by means of Agrobacterium tumefaciens infection to obtain a tissue-specific PtoPIN1a overexpressing transgenic plant, and subsequently, a plant with a relatively high expression level is selected, by means of positive identification, for phenotype observation. Results show that compared with the wild type in the same period, the PtoPIN1a transgenic plant has an increased plant height, a thicker stem and a significantly increased aboveground biomass. By means of the provided technical cultivation method, a high-biomass high-quality poplar variety is cultivated. The method is of great significance for future molecular breeding to cultivate excellent poplar varieties.

The invention relates to the technical field of teasel breeding, and discloses a teasel germplasm resource multi-character collaborative screening breeding method, which comprises the following steps: multi-omics data acquisition: extracting teasel germplasm leaf DNA (deoxyribonucleic acid), and capturing exon regions of disease resistance and metabolism related genes by adopting a targeted sequencing technology to obtain an SNP (single nucleotide polymorphism) marker matrix; the method comprises the following steps: collecting root extracts, detecting medicinal components through ultra-high performance liquid chromatography-mass spectrometry, and constructing a metabolite abundance matrix; measuring phenotype data of plant height, root length, root weight, root rot morbidity and survival rate under drought stress; key site and metabolite correlation analysis: constructing a Bayesian network containing SNP markers, metabolite and phenotypes by using a graph database, and carrying out cross validation through a leave-one-out method. The dipsacus asper germplasm resource multi-character collaborative screening breeding method aims at solving the problem that a traditional breeding method lacks systematic integration analysis of genomes and metabolic pathways in dipsacus asper multi-character collaborative improvement.

Some embodiments relate to receiving a natural language question for a study, such as a retrospective study. Some embodiments include formatting the question according to a defined question format. Some embodiments include utilizing a phenotype library to map information provided in the question to specific medical codings. Some embodiments include retrieving patient health record data and performing automated statistical analysis. Some embodiments include summarizing study data. Some embodiments include identifying and summarizing relevant published articles.

The invention discloses a wheat thousand seed weight identification method based on an SNP molecular marker and application, and relates to the technical field of genetic engineering, and the wheat thousand seed weight identification method comprises the following steps: A1, taking DNA of a wheat sample to be detected as a template, and carrying out PCR amplification by adopting a KASP primer combination according to any one of claims 2-3 or a kit according to claim 4; a2, judging the thousand seed weight of the wheat sample to be detected according to an amplification result. An SNP site related to the thousand seed weight of a plant is obtained through phenotypic difference analysis and exon capture analysis of the thousand seed weight, a corresponding KASP primer combination is designed for the SNP site, and the thousand seed weight of wheat can be rapidly identified through PCR amplification and genotype analysis: the thousand seed weight of AA genotype wheat is significantly higher than that of GG genotype wheat; the method provides an accurate molecular marking tool for high-yield breeding of wheat, has the advantages of high efficiency, accuracy and suitability for high-throughput detection, and has important significance for increasing the yield of wheat.

The invention discloses a breeding pressure simulation-based high-density-resistant penaeus monodon breeding method and system. The method comprises the following steps: determining pressure influence factors of penaeus monodon breeding through data retrieval, and extracting key environment variables by using principal component analysis; setting a plurality of groups of breeding pressure gradients, constructing a plurality of test groups by adopting an orthogonal test method, and collecting environment and phenotype data to evaluate the breeding effect and form a test data set. And constructing a breeding analysis model, and analyzing breeding evolution characteristics by constructing a correlation path and a topological structure chart of the breeding pressure gradient, the breeding effect and the monitoring data. And analyzing breeding demand information, inputting the breeding demand information into the model to obtain candidate breeding schemes, performing breeding feature extraction and cost evaluation on the candidate schemes, and finally recommending an optimal breeding scheme. The overall breeding benefit and production safety are improved, and the breeding period and complexity are reduced.

The invention belongs to the technical field of pepper planting, and discloses a pepper multi-branch gene CaBr1 linked KASP molecular marker as well as a primer, a kit and application thereof, the KASP molecular marker takes pepper S8 (zhangshuang) as a reference genome, and the single nucleotide polymorphism at the 103175415th basic group on the No.8 chromosome is subjected to basic group G-to-A replacement. A chromosome region closely linked with multiple branches of the pepper is obtained by using a BSA (Bovine Serum Albumin) population positioning method, a molecular marker is developed in a candidate region to determine a candidate gene CaBr1, a KASP molecular marker is screened according to base mutation of the candidate gene, and the marker is used for carrying out genotype identification on 90 randomly sampled single plants of an F2 population, and the coincidence rate reaches 100%. The result not only contributes to pepper branch phenotype identification and assistant breeding, but also lays a foundation for map-based cloning of the branch gene and analysis of a molecular mechanism of pepper plant type regulation.

The invention discloses a biological complex trait key gene identification method, device and equipment and a medium, and the method comprises the following steps: obtaining biological reference genome data, and carrying out orthologous gene class group division on a protein sequence coded by the biological reference genome data; determining a characteristic value data set according to the number of the subgroup sequence and the gene number of the subgroup sequence obtained after division; obtaining each piece of biological phenotype data; utilizing a machine algorithm model to construct a phenotype matrix according to the biological phenotype data and the characteristic value data set so as to train a phenotype prediction model, and determining the influence weight of each subgroup sequence on the biological phenotype according to the phenotype prediction model; and according to each influence weight, determining a key gene for regulating and controlling the biological complexity. The method provided by the invention can realize more accurate, efficient and comprehensive identification of key genes of prokaryotes and high biological complexity characters.