14 results about "Phenome" patented technology

The invention discloses a method for assisting hawthorn breeding based on phenotypic omics, and relates to the technical field of phenotypic omics assisted hawthorn breeding. Comprising the following steps: S1, acquiring phenotypic data of normally growing hawthorns by using a high-throughput imaging system and a phenotypic analysis system, and detecting and analyzing various characters of the hawthorns; s2, establishing a hawthorn multi-dimensional phenotypic group database, and obtaining hawthorn breeding key character quantitative indexes; s3, constructing a phenotype-genotype correlation model according to the obtained hawthorn breeding key character quantitative indexes, and mining new functional genes to realize early prediction of breeding; and S4, creating a phenotype exponential weighting algorithm for parent matching. The method for assisting hawthorn breeding based on phenotypic omics is established, the problems of long hawthorn breeding period and high cost are solved, and the hawthorn breeding efficiency is effectively improved.

This invention discloses a method for breeding salt-tolerant maize based on root function and artificial intelligence prediction, belonging to the field of crop breeding. It includes constructing a core germplasm resource bank, conducting high-throughput root phenotypic identification under 150mM NaCl stress, screening superior root systems based on quantitative thresholds such as a root-to-shoot ratio increase of more than 20% and lateral root density exceeding the mean by 1.5 times the standard deviation, performing dynamic multi-omics analysis at seven time points (0h, 3h, 6h) after stress to identify early response markers within 3-12h after stress, creating core parents by combining molecular markers and root traits, integrating seedling phenotypic, omics, and genotypic data, training an AI model with a prediction accuracy R² of no less than 0.65, and using the model to eliminate the last 50% of predicted potential materials in the early stages of breeding to guide parent selection. This invention achieves a paradigm shift from empirical screening to precise prediction, significantly improving breeding efficiency and directionality.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a contrastive molecular-phenomic embedding model that learns joint latent space embeddings between molecular structures and phenomic images to generate molecular-phenomic embeddings that represent molecular impacts on cellular functions. Indeed, the disclosed systems can utilize phenomic image embeddings generated from a pretrained phenomic image encoder model and corresponding molecular structural embeddings with a contrastive molecular-phenomic embedding model to learn a joint latent space between molecular structures and phenomic images utilizing a modified rank-n-contrast loss with a learnable temperature parameter. In addition, the disclosed systems can utilize molecular structures and / or phenomic images with the contrastive molecular-phenomic embedding model to generate molecular-phenomic embeddings that enable a variety of molecular inferences (e.g., similar molecule determinations, similar phenomic image determinations, phenotypic impact determinations from particular molecules, molecular activity classifications, and / or inactive region filtering).

The application discloses a method for predicting and breeding Macrobrachium rosenbergii chela ratio based on phenomics analysis, comprising the following steps: selecting a plurality of Macrobrachium rosenbergii for multi-generation breeding, recording pedigree data of each individual, and constructing a Macrobrachium rosenbergii breeding database; extracting the phenotypic characteristics of Macrobrachium rosenbergii at different breeding stages, analyzing the relationship between the growth index and the chela ratio, and evaluating the genetic force to construct a Macrobrachium rosenbergii phenomics network; analyzing the growth path of Macrobrachium rosenbergii, identifying abnormal events during breeding, and generating a Macrobrachium rosenbergii growth trend network; constructing a Macrobrachium rosenbergii phenotype knowledge graph, and constructing a Macrobrachium rosenbergii chela ratio prediction model; collecting early phenotype data of the Macrobrachium rosenbergii population to be bred, making breeding decisions, and recommending breeding paths for the breeding individuals by using the Macrobrachium rosenbergii phenotype knowledge graph. Whether the breeding path is deviated in the breeding process is analyzed at the same time, and a correction scheme is formulated and pushed, so that the breeding cycle of Macrobrachium rosenbergii is shortened, and the accuracy and efficiency of breeding are improved.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for training and utilizing machine learning models to generate structure-phenomics relationship predictions for cell perturbations. In particular, in some embodiments, the disclosed systems receive a query chemical compound. In addition, in some embodiments, the disclosed systems generate a compound structure feature representation for the query chemical compound. Moreover, in some embodiments, the disclosed systems generate, utilizing a structure-phenomics relationship machine learning model, a phenomic similarity prediction for the compound structure feature representation and a target perturbation.

The disclosure provides, in various embodiments, compositions, such as polypeptides, polynucleotides, gene editing systems, small molecules, vectors or host cells, that comprise and / or modulate expression or activity of immune regulation-associated proteins, such as cytokines. The disclosure also provides, in various embodiments, methods of treating a disease or condition (e.g., a disease or condition associated with the Genome-Wide Association Study (GWAS), the Cancer Genome Atlas (TCGA), whole genome sequencing, phenome-wide association study (PheWAS), expression quantitative trait locus (cQTL) studies, or a combination thereof) using an agent that comprises and / or modulates expression or activity of an immune regulation-associated protein, and methods of identifying said agent.

The invention discloses a kidney phenotype map-based elderly chronic kidney disease progress prediction model construction method, and belongs to the technical field of disease prediction. The method comprises the following steps: S1, determining a selection standard and an exclusion standard, and defining a research population; determining a research type and a blind method design; s2, four phenotype databases are constructed in a standardized mode through the whole process of data acquisition, processing and storage, and multi-dimensional data from macroscopic clinical information to microscopic molecular markers are covered; s3, making a follow-up plan and defining an end point; s4, determining the minimum sample size based on double logics of risk factor screening and the outcome occurrence rate; s5, constructing a model by adopting a classical machine learning algorithm, and evaluating the performance of the model through a multi-dimensional index; constructing a model of various different phenotype combinations; and S6, model comparison and screening: screening an optimal clinical application model from the constructed various models through specificity and sensitivity comparison. According to the invention, a multi-dimensional, high-precision and landing prediction model can be obtained.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a contrastive molecular-phenomic embedding model that learns joint latent space embeddings between molecular structures and phenomic images to generate molecular-phenomic embeddings that represent molecular impacts on cellular functions. Indeed, the disclosed systems can utilize phenomic image embeddings generated from a pretrained phenomic image encoder model and corresponding molecular structural embeddings with a contrastive molecular-phenomic embedding model to learn a joint latent space between molecular structures and phenomic images (with an inter-sample similarity aware loss (S2L), explicit and implicit concentration encoding, and under sampling of inactive molecular data). In addition, the disclosed systems can utilize molecular structures and / or phenomic images with the contrastive molecular-phenomic embedding model to generate molecular-phenomic embeddings that enable a variety of molecular inferences (e.g., similar molecule determinations, similar phenomic image determinations, phenotypic impact determinations from particular molecules, and / or molecular activity classifications).

The present disclosure relates to systems, non-transitory computer-readable media, and methods for training and utilizing machine learning models to generate structure-phenomics relationship predictions for cell perturbations. In particular, in some embodiments, the disclosed systems receive a query chemical compound. In addition, in some embodiments, the disclosed systems generate a compound structure feature representation for the query chemical compound. Moreover, in some embodiments, the disclosed systems generate, utilizing a structure-phenomics relationship machine learning model, a phenomic similarity prediction for the compound structure feature representation and a target perturbation.

The present disclosure relates to systems, non-transitory computer-readable media, and methods that analyze gene perturbation machine learning embeddings and clinical observation data sets utilizing machine learning, explainability models, and causal discovery models to generate causal predictions between one or more genes and clinical outcomes. Indeed, in one or more implementations, the disclosed systems identify gene perturbation embeddings generated from cells exposed to perturbations. For instance, the disclosed systems select a cluster of genes from a plurality of genes by applying a clustering model to the gene perturbation embeddings. In some instances, the disclosed systems select gene targets from the cluster of genes by using a machine learning classification model trained on a plurality of features of the clinical observation data set. Moreover, in some instances, the disclosed systems generate the causal prediction from the gene targets and the clinical observation data set utilizing a causal discovery model.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a contrastive molecular-phenomic embedding model that learns joint latent space embeddings between molecular structures and phenomic images to generate molecular-phenomic embeddings that represent molecular impacts on cellular functions. Indeed, the disclosed systems can utilize phenomic image embeddings generated from a pretrained phenomic image encoder model and corresponding molecular structural embeddings with a contrastive molecular-phenomic embedding model to learn a joint latent space between molecular structures and phenomic images (with an inter-sample similarity aware loss (S2L), explicit and implicit concentration encoding, and under sampling of inactive molecular data). In addition, the disclosed systems can utilize molecular structures and / or phenomic images with the contrastive molecular-phenomic embedding model to generate molecular-phenomic embeddings that enable a variety of molecular inferences (e.g., similar molecule determinations, similar phenomic image determinations, phenotypic impact determinations from particular molecules, and / or molecular activity classifications).

This application discloses a method for screening candidate genes for drought-resistant traits in wild soybean, comprising: collecting phenotypic, transcriptomic, genomic, and metabolomic data from a wild soybean population, preprocessing them to obtain a multi-omics feature matrix set; defining gene nodes, metabolite nodes, and phenotype nodes based on the multi-omics feature matrix set, constructing a node feature matrix, constructing gene-gene edge sets, gene-metabolite edge sets, and constructing metabolite-phenotype edge sets to form a gene-metabolite-phenotype heterogeneity graph; constructing a graph convolutional-graph attention fusion network, inputting the heterogeneity graph, and outputting gene importance score vectors; sorting all gene importance scores in order, selecting genes within the preset ranking as candidate genes and performing interpretability analysis; experimentally validating the candidate genes, calculating the correlation between the importance score and the validation results, and optimizing the network and updating the candidate genes if the criteria are not met. This application can improve the accuracy and reliability of candidate gene screening.

The present invention relates to methods of determining the clinical phenotype of patients suffering from hidradenitis suppurativa (HS), a chronic inflammatory skin disease characterized by recurrent nodules, abscesses, and sinus tracts in the intertriginous areas. The invention is based on the discovery of a set of biomarkers that are differentially expressed in the hair-follicles of HS patients. The invention provides methods of measuring the levels of these biomarkers in biological samples from HS patients, and using them to classify the patients into different phenotypic groups. The invention also provides methods of selecting or adjusting the treatment for HS patients based on their phenotypic group. The invention thus offers a novel and useful tool for the personalized diagnosis and management of HS.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a contrastive molecular-phenomic embedding model that learns joint latent space embeddings between molecular structures and phenomic images to generate molecular-phenomic embeddings that represent molecular impacts on cellular functions. Indeed, the disclosed systems can utilize phenomic image embeddings generated from a pretrained phenomic image encoder model and corresponding molecular structural embeddings with a contrastive molecular-phenomic embedding model to learn a joint latent space between molecular structures and phenomic images (with an inter-sample similarity aware loss (S2L), explicit and implicit concentration encoding, and under sampling of inactive molecular data). In addition, the disclosed systems can utilize molecular structures and / or phenomic images with the contrastive molecular-phenomic embedding model to generate molecular-phenomic embeddings that enable a variety of molecular inferences (e.g., similar molecule determinations, similar phenomic image determinations, phenotypic impact determinations from particular molecules, and / or molecular activity classifications).