12 results about "Genomic Profile" patented technology

Disclosed herein are methods and systems of determining a cancer status or an organ health status of a subject, the method comprising: obtaining a biological sample obtained or derived from the subject; enriching a population of cells in the biological sample, wherein the population of cells comprises stem cells and / or progenitor cells; extracting nucleic acids from the enriched population of cells; assaying the extracted nucleic acids to generate at least one of a transcriptomic profile of the subject, a genomic profile of the subject, and / or an exomic profile of the subject; computer processing the at least one of the transcriptomic profile of the subject, the genomic profile of the subject, and / or the exomic profile of the subject; and determining, based at least in part on the computer processing, the cancer status or the organ health status of the subject, wherein the cancer status or the organ health status comprises a presence or an absence of an organ and / or a tissue impacted by the cancer or impacted by a disease, disorder, or condition.

The present invention relates to a specialized AI-driven data analytics platform tailored for optimizing engineered T cell therapies in patients, particularly those undergoing treatment for cancer, autoimmune diseases, and inflammatory conditions. Unlike general-purpose AI systems, this platform integrates advanced machine learning, deep learning, and fuzzy logic algorithms to continuously analyze and prioritize real-time data from multiple sources, including patient monitoring systems, laboratory tests, imaging modalities, wearable devices, and genomic profiles. The platform is specifically designed to predict and manage adverse events unique to T cell therapies, such as Cytokine Release Syndrome (CRS) and Tumor Lysis Syndrome (TLS), offering clinicians real-time, personalized guidance that dynamically adjusts treatment protocols during and after T cell infusion. The system's adaptive learning capabilities allow it to evolve by incorporating clinical feedback and patient outcomes, continuously refining its predictive models to enhance precision and effectiveness. By providing robust support for managing complex side effects and delivering actionable recommendations, this invention marks a significant advancement in the application of AI to oncology, offering a highly specialized, innovative approach to enhancing the safety and efficacy of engineered T cell therapies.

This invention discloses a promoter screening system, comprising a P&E-Vector vector and an E-Vector vector. The P&E-Vector vector sequence is shown in SEQ ID No:1, and the E-Vector vector sequence is shown in SEQ ID No:2. The P&E-Vector vector enables efficient identification and high-throughput screening of promoters, while the E-Vector vector effectively eliminates the influence of enhancers. This screening system is highly sensitive and simple to operate, providing important technical support for constructing whole-genome maps of promoters and elucidating their characteristics and mechanisms.

This invention discloses a prognostic model and construction method for cervical squamous cell carcinoma, belonging to the fields of bioinformatics and oncology. The construction method is based on a set of genes related to programmed cell death, integrating multi-omics data of cervical squamous cell carcinoma patients from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and the Cancer Genome Characterization Project (CGCI). After standardized preprocessing, differential expression analysis, bootstrap resampling combined with univariate Cox regression for initial screening, consensus screening using multiple machine learning algorithms, and finally, multivariate Cox proportional hazards regression analysis to identify three core independent prognostic genes and construct a risk scoring model. This model has undergone multi-dimensional validation and optimization, demonstrating robust predictive performance. The method of this invention is standardized and highly reproducible, and the constructed model has high accuracy and strong generalization ability, providing a reliable tool for individualized prognostic assessment and clinical decision-making for cervical squamous cell carcinoma patients.

The application provides a multi-omics gene mining technology-based method for detecting and regulating rice grain length GW5 The application relates to the technical field of biology, and relates to application of gene promoter methylation in regulation of rice grain length. The application is based on a pan-genome micro-core germplasm population, integrates multi-dimensional omics data such as genomes and epigenomes, constructs an epigenome atlas of rice, and uses stable methylation polymorphic markers in the atlas to combine with a rice grain width phenotype to perform multi-omics correlation analysis, successfully mines a new epiallele of a gene, discovers GW5 a new epiallele of a gene, and discovers GW5 that a high-methylation and low-methylation epigenetic state exists in an upstream promoter region of the gene in different rice germplasm backgrounds, and that after demethylation editing is performed on the region, a phenotype of significantly increased grain length is exhibited. Therefore, according to GW5 a methylation level of a promoter of the gene, detection of a rice grain length trait can be realized, and by regulating GW5 a methylation level of a promoter of the gene, regulation of a rice grain length trait can be realized.

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 relates to the technical field of genome data analysis, in particular to a whole genome map splicing method for structural variation detection, which comprises the steps of data preprocessing, feature map generation, multi-scale splicing frame construction, local optimization module design, global consistency correction, structural variation marking and result output and storage. According to the method, the data processing efficiency is improved through a distributed computing framework and a Hash index technology, the splicing precision and efficiency are improved by adopting deep learning to extract features and combining a multi-scale splicing framework, global consistency correction is realized by utilizing a graph theory method, and structural variation is detected through a sliding window scanning algorithm. According to the method, the splicing efficiency and accuracy of large-scale genome data can be remarkably improved, and reliable support is provided for structural variation detection.

An improved method for Next Generation Sequencing which relies on the presence of the same distinct unique molecular identifier (UMI) located at each end of a linear nucleic acid molecule so that sequence reads of approximately 2 kb or longer are obtained, and which allows generation of a genomic map without the need of a reference sequence.

The invention discloses a promoter screening system. The system comprises a Pamp; an E-Vector carrier, an E-Vector carrier, and a Pamp; the sequence of the E-Vector vector is as shown in SEQ ID No: 1, and the sequence of the E-Vector vector is as shown in SEQ ID No: 2. The method comprises the following steps: passing through Pamp; according to the present invention, the E-Vector vector can efficiently identify the promoter and perform high-throughput screening on the promoter, the influence of the enhancer can be effectively eliminated through the E-Vector vector, the screening system has characteristics of high sensitivity and simple operation, and the important technical support is provided for the construction of the promoter whole genome map and the analysis of the promoter characteristics and mechanism.

The invention discloses a cancer genome map key gene positioning method, which comprises the following steps of: 1) for each case in a case set, generating pathological image embedding and text embedding of the case; for each gene in a gene expression matrix corresponding to the case set, a standardized expression vector of the gene is spliced with pathological image embedding and text embedding of each case into a unified state vector, and the unified state vector is used as initial observation of an intelligent agent in a reinforcement learning algorithm; (2) each agent generates a key gene subset of current positioning according to the action adopted by the initial random strategy of the agent; 3) each agent encodes the current key gene subset into a current state input DQN to obtain a corresponding action update key gene subset code and award; updating the next state of the intelligent agent; the environment knowledge explored by the intelligent agent meets a set condition; and 4) when each agent meets a set convergence condition, taking the currently positioned key gene subset as a key gene set.

This disclosure provides, among other things, methods for compiling and using a database for identifying one or more therapeutic interventions for a cancer and / or the efficacy of a therapeutic intervention for subjects with a tumor genomic profile. The database may include, for each of a plurality of subjects having cancer: (i) tumor genomic testing data, including somatic alterations, collected at two or more time intervals per subject via serial biopsy of cell-free DNA, (ii) one or more therapeutic interventions administered to each of the subjects at one or more times; and (iii) efficacy of the therapeutic interventions.

Methods, systems, apparatuses, devices, and computer program products are described. A system may use a rules engine and a reinforcement learning artificial intelligence (AI) model to recommend treatment options for a patient. In some examples, the AI model may be trained for a specific diagnosis. The system may receive patient information including the patient's diagnosis, genomic profile (e.g., partial or full genomic information), and treatment history. The system may input the genomic profile into the rules engine to determine any relevant treatment modifications for the user based on biomarkers in the genomic profile. The system may additionally input the patient information into the AI model to determine a set of treatment option recommendations and corresponding confidence metrics. The system may send the treatment option recommendations (e.g., which in some cases may be modified based on the output of the rules engine) to a user device for display.