24 results about "Molecular phenotype" patented technology

The invention belongs to the technical field of gene detection and gene molecular breeding, and particularly relates to a meat duck whole genome molecular probe combination based on molecular phenotype screening, a 50K gene chip and application thereof. The molecular probe combination and the gene chip of the marker site combination for meat duck whole genome breeding simultaneously cover 7 representative meat duck varieties and 71 economic characters, have richer polymorphism and higher pertinence in meat duck groups, and are lower in cost and higher in speed compared with high-throughput sequencing detection; the breeding chip is designed according to the growth, feed efficiency, slaughtering, breeding, egg quality and various molecular phenotypes of the meat ducks, and compared with a high-throughput sequencing technology, the breeding chip is higher in seed selection accuracy, has higher breeding value, can be widely applied to breeding genotype detection of the meat ducks, and can be used for detecting the breeding genotypes of the meat ducks. And the method has creative significance in the aspect of meat duck genome selective breeding.

Described here are methods to utilize genomic and epigenomic detection platforms to provide quantitative metrics, including those representative of molecular phenotypes, in a variety of iimmuno-oncology applications, including those related to the characterization of patient responsiveness, treatment efficacy, adverse events among otheres. In various embodiments, described herein is an example of generating prognostic and t outcomes of a cfDNA-derived promoter methylation score in predicting ICI benefit.

The invention relates to a disease-specific quantitative trait locus identification method based on multi-omics integration. The method comprises the following steps: acquiring variation sites of whole genome sequencing data of a target object, and molecular phenotypes and molecular abundance of molecular phenotype data; determining an association significance probability value of an association pair formed by the variation point and the molecular phenotype based on the variation point and the molecular abundance, and screening a first association pair from the association pair based on the association significance probability value and condition analysis; determining a consistent second association pair in the normal association pair and the disease association pair, and determining a third association pair with a disease interaction effect in the second association pair; calculating a first effect estimation value and a second effect estimation value of each third association pair; and based on the first effect estimation value and the second effect estimation value of the third correlation pair, determining a target correlation pair related to the Parkinson's disease, and taking the target correlation pair as the identified quantitative trait site. By adopting the method, the Parkinson's specific pathogenic heritable variation can be accurately identified.

The invention discloses a mental disease typing method and system integrating DNA methylation and neuroimaging, and the method is a neuroimaging biological annotation method integrating DNA methylation and brain connection group data, and comprises the steps: recognizing brain network features related to a specific molecular phenotype in a whole brain range through a machine learning model; and mechanism-sensitive layering of the mental disorder heterogeneity group is realized. The method does not need to depend on a prior classification or hypothesis mechanism, can be suitable for different types of mental disorder people, provides technical support for exploring potential biological mechanisms and identifying targeted therapy groups, and has high generalizability and clinical application prospects.

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 invention discloses a construction method of a humanized diffuse malignant peritoneal mesothelioma mouse model, the mouse model obtained by the construction method and application of the mouse model. The construction method comprises the following steps: constructing a DMPM subcutaneous tumor NOG mouse model by utilizing tumor blocks from human malignant peritoneal mesothelioma; the method comprises the following steps: constructing a huDMPM PBMC-NOG-dko mouse model by utilizing a tumor tissue from a DMPM subcutaneous tumor NOG mouse model; the obtained humanized diffuse malignant peritoneal mesothelioma mouse model completely retains histopathologic characteristics and molecular phenotypes of primary tumors, has a humanized immune system, and can successfully reproduce tumor-immune system interaction characteristics. The method can be used for simulating a tumor microenvironment regulation mechanism of the DMPM, evaluating drugs or therapy of the DMPM and the like.

The application discloses a multi-modal low-rank adaptive cognitive function estimation method based on physiological-molecular phenotype, belongs to the technical field of cognitive function estimation, and first collects physiological-molecular phenotype, dynamic physiological signals and cognitive test time information of a subject in a plateau hypoxic environment, and constructs a 28-dimensional multi-modal input feature vector after preprocessing; then, a hybrid expert base model is pre-trained based on a source cognitive task with the largest data volume; finally, the base model parameters are frozen for a new target task, a LoRA low-rank adaptive technology is introduced, only a small number of parameters are trained to complete cross-task adaptation, and the cognitive task reaction time and accuracy are output to realize estimation. The application realizes unified estimation of eight types of core cognitive functions, has high migration efficiency, excellent estimation precision and stability, and can be accurately applied to real-time monitoring of cognitive functions of workers in a plateau hypoxic environment.

The present application relates to the technical field of protein genomics analysis, and particularly relates to a protein gene multiomics analysis method, which comprises: S1. sample collection and pretreatment: tumor tissue and paired normal adjacent tissue are selected as samples, and the samples are frozen and crushed; S2. sample multiomics detection: the frozen and crushed samples are subjected to genomics detection and proteomics detection; S3. sample multiomics data processing and analysis: the genomics detection result and the proteomics detection result of the samples are obtained, and tumor molecular characteristics are identified through multiomics data integration analysis.The present application carries out systematic protein genomics analysis on LCNEC tumor tissue and NATs. The research not only reveals the genomic abnormality characteristics of LCNE and disease-related molecular phenotypes, but also deepens the scientific cognition of patient stratification logic in targeted therapy strategies. Further research finds that IL33, as a new key therapeutic biomarker, is related to T cell infiltration and has anti-tumor activity.

The application relates to a thymoma epithelial cell subpopulation with neuromuscular characteristics and application, and a thymoma epithelial cell subpopulation with neuromuscular characteristics is obtained through single clone dilution culture screening from a thymoma cell line Thy0517 of a patient with myasthenia gravis (MG) in combination, and a thymoma epithelial cell with neuromuscular characteristics in the cell subpopulation is named as Thymus_NMi; the cell subpopulation has synapse-like structures and neuromuscular adhesion characteristics, and efficiently expresses genes participating in neural cell adhesion and synapse connection, highly integrates neuromuscular double characteristics, and simulates key pathological characteristics of abnormal thymus-induced immune tolerance of MG patients in a molecular phenotype and physiological function, so that a cell model closest to a real clinical state is provided for exploring a myasthenia gravis occurrence mechanism.

In X-linked dystonia-parkinsonism (XDP), an inherited SVA retrotransposon insertion in the TAF1 gene disrupts gene transcription, leading to a rare neurodegenerative disorder. The mechanism underlying aberrant TAF1 transcription remains elusive. We found that 5-ALA, a 5-ALA ester, a porphyrin or a pharmaceutically acceptable salt thereof for use is capable of mitigating the molecular phenotypes associated with the G4-quadruplex structures in the XDP- SVA in XDP-patient cells. The present invention demonstrates that treatment with 5-ALA, a 5-ALA ester, a porphyrin or a pharmaceutically acceptable salt thereof for use thereof significantly neutralized the main molecular phenotypes linked to XDP. The invention provides means and methods for the treatment of XDP or an individual carrying a genetic modification associated with XDP.

The present application belongs to the technical field of gene detection and gene molecular breeding, and particularly relates to a meat duck whole genome molecular probe combination based on molecular phenotype screening, a 50K gene chip and application thereof. The present application provides a molecular probe combination of a marker site combination for meat duck whole genome selection and a gene chip, which simultaneously covers 7 representative meat duck breeds, 71 economic traits, has rich polymorphism in a meat duck population, is more targeted, and has lower detection cost and faster speed compared to high-throughput sequencing. The present application mines relevant sites for meat duck growth, feed efficiency, slaughter, reproduction, egg quality and various types of molecular phenotypes, designs a breeding chip, has higher selection accuracy compared to high-throughput sequencing technology, has high breeding value, can be widely applied to genotype detection of meat duck breeding, and has a pioneering significance for meat duck genome selection breeding.

The application relates to a thymoma epithelial cell subpopulation with neuromuscular characteristics and application, and a thymoma epithelial cell subpopulation with neuromuscular characteristics is obtained through single clone dilution culture screening from a thymoma cell line Thy0517 of a patient with myasthenia gravis (MG) in combination, and a thymoma epithelial cell with neuromuscular characteristics in the cell subpopulation is named as Thymus_NMi. The cell subpopulation has synapse-like structures and neuromuscular adhesion characteristics, and efficiently expresses genes participating in neural cell adhesion and synapse connection, highly integrates neuromuscular double characteristics, and simulates key pathological characteristics of abnormal thymus-induced immune tolerance of MG patients in a molecular phenotype and physiological function, so that a cell model closest to a real clinical state is provided for exploring a myasthenia gravis occurrence mechanism.

A method for identifying a pathogen includes obtaining a biological specimen. The method may include dividing the specimen into at least a first sample and a second sample. The method may include identifying at least one pathogen in the first sample via genetic analysis. The method may include exposing the second sample to a panel of antimicrobial agents at known concentrations based on the at least one pathogen. The method may include performing a polymerase chain reaction (PCR) assay on the second sample after exposure of the second sample to the panel of the antimicrobial agents, wherein the PCR assay includes oligonucleotides that amplify a nucleic acid sequence for one or more of the identified pathogens. The method may include analyzing a result of the PCR assay.

The application discloses a kind of based on pathological whole section image's spatial molecular phenotype super-resolution prediction method, device and storage medium, the method includes: obtaining the pathological whole section image of sample to be analyzed;Pathological whole section image is detected and is cut to block processing to the organization area, generate multiple image blocks containing spatial coordinates;Using trained prediction network, multiple image blocks are handled, obtain pixel level or spot level gene expression / pathway activity dense prediction feature map;According to its spatial position, dense prediction feature map is filled back to whole section coordinate system and is spliced, obtains whole section level super-resolution spatial molecular atlas.The present application can be widely used in tumor microenvironment spatial phenotype analysis, virtual molecular staining, risk stratification and prognosis evaluation etc.Scenarios, with no additional experiment, low cost, high resolution, deployability is strong and the like advantages.

The present disclosure provides methods and systems that can ascertain how genetic variants impact molecular phenotypes. Such methods and systems may use additional conservation information. In an aspect, the present disclosure provides a method for training a molecular phenotype neural network (MPNN), comprising: (a) providing a molecular phenotype neural network (MPNN) comprising one or more parameters; (b) providing a training data set comprising (i) a set of one or more inputs comprising biological sequences and (ii) for each input in the set of one or more inputs, a set of one or more molecular phenotypes corresponding to the input; (c) configuring the one or more parameters of the MPNN based on the training data set to minimize a total loss of the training data set, thereby training the MPNN; and (d) outputting the one or more parameters of the MPNN.

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 application is a breast pathological image molecular typing prediction method based on a multi-attribute embedding model, and the steps are as follows: step one: a multi-attribute classifier is constructed based on the deep features of the image blocks of digital pathological sections to obtain the instance-level molecular level probability distribution; step two: a feature embedding module is constructed, and the global feature is constructed based on the instance-level deep features and the strongest representation block; step three: a Transformer-MLP classifier is constructed to perform slice-level molecular typing prediction on the global feature; and step four: a two-level classification loss with multi-label constraints is constructed to jointly train the network. The application can construct the correlation between the morphological features of the breast pathological image and the immunohistochemical molecular phenotype, and simultaneously, in view of the weak label problem of the WSI data, a weakly supervised classification method based on multi-attribute feature embedding is designed to realize accurate prediction of the immunohistochemical molecular typing of breast cancer. The application can be combined with various computer pathological auxiliary analysis systems, and has wide market prospects and application value.

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 invention provides a gene-environment interaction in-vitro model based on reward pressure stimulation as well as a construction method and application of the gene-environment interaction in-vitro model. According to the method, a human-derived midbrain organ rich in dopaminergic neurons is placed on a microelectrode array, and continuous reward pressure load is simulated by applying electrical stimulation corresponding to an explosive discharge frequency of the dopaminergic neurons, so that a chronic environmental stress condition is reconstructed. The electrical stimulation can selectively induce a patient-derived organ of a gene-environment interaction related disease to generate disease-related electrophysiology and molecular phenotypic changes, and reflects the neuroplasticity defect under the synergistic effect of gene susceptibility and environment pressure. Compared with the traditional condition that low-frequency stimulation induces long-term inhibition or no stimulation is applied, the method has unique effectiveness in the aspect of inducing the related pathological characteristics of depression. The method can be used for constructing in-vitro models of gene-environment interaction related diseases such as depression, and is suitable for drug screening and development and efficiency evaluation of diagnosis and treatment strategies.

The invention relates to a method based on Hamp; the invention discloses a method for cross-modal prediction of immunohistochemical molecular phenotypes of E digital pathological sections. The problems that in the prior art, an immunohistochemical molecular phenotype detection method is long in consumed time and poor in diagnosis accuracy are solved. The method comprises the following steps: S1, data preprocessing: performing Hamp processing on the data according to annotation information; e, segmenting the digital pathological section and distributing labels; s2, using transfer learning to train a convolutional neural network model CNN as a first-stage model, and outputting a prediction result; s3, post-processing a prediction result output by the first-stage model, obtaining a slice level label according to a label distribution strategy, and inputting a second-stage model by taking the prediction result as a tumor slice; and S4, cross-modal prediction: distributing labels of nine biomarkers for each section to train a second-stage model. The method has the advantages that the diagnosis accuracy of low-differentiation lung cancer subtypes and the classification accuracy of complex and heterogeneity tumor tissues are improved, and the acceptability and the transformability of an algorithm are improved.

The invention relates to the technical field of large-scale data visualization and intelligent sampling, in particular to an intelligent self-adaptive rasterization genome association analysis and molecular phenotype QTL (Quantitative Trait Locus) data visualization method and a visualization system of the intelligent self-adaptive rasterization genome association analysis and molecular phenotype QTL data. Mapping the data points to a preset grid unit; calculating statistical feature information in each grid unit, wherein the statistical feature information comprises an extreme value, a point density and an attribute aggregation result; on the basis, significance judgment, high-density judgment and random sampling judgment are sequentially executed through a decision control module, and whether the representative points of the corresponding grid units are reserved or not is determined. According to the method, the number of data points and calculation overhead can be remarkably reduced, meanwhile, key remarkable signals and high-density regional features are effectively reserved, important information is prevented from being lost, and the method is suitable for efficient visualization and analysis scenes of genome correlation analysis, QTL data display and other large-scale high-dimensional data.

The invention provides a wheat quality trait gene positioning method and system based on correlation analysis. Comprising the following steps: acquiring whole genome typing data of an associated group; applying a standardized perturbation sequence to the associated population, and collecting a time-sequenced biological sample to obtain a dynamic molecular phenotype; performing parameterization processing on the dynamic molecular phenotype to obtain a group of quantitative dynamic response spectrum parameters including kinetic parameters and stress memory parameters; and taking the newly created parameters as phenotypes, and carrying out whole genome association analysis on the phenotypes and whole genome typing data so as to locate gene loci for controlling dynamic response characteristics. According to the method, the relation between laboratory dynamic response and field character stability can be established through causal intermediary analysis. According to the method, genetic loci for controlling a character dynamic process and environmental adaptability can be positioned, and a new technical approach is provided for precise breeding of crops.

A method for identifying a pathogen includes obtaining a biological specimen. The method may include dividing the specimen into at least a first sample and a second sample. The method may include identifying at least one pathogen in the first sample via genetic analysis. The method may include exposing the second sample to a panel of antimicrobial agents at known concentrations based on the at least one pathogen. The method may include performing a polymerase chain reaction (PCR) assay on the second sample after exposure of the second sample to the panel of the antimicrobial agents, wherein the PCR assay includes oligonucleotides that amplify a nucleic acid sequence for one or more of the identified pathogens. The method may include analyzing a result of the PCR assay.

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).