AI-powered spatial integration of multi-omics datasets using probabilistic alignment for comprehensive tissue atlas construction and cellular state mapping.
Skills in this repository
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SkillsMP has collected 810 skills from mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-. Open a skill to review its source and details.
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Annotate scRNA-seq
AI-powered spatial epigenomics analysis combining chromatin accessibility, histone modifications, and DNA methylation with spatial coordinates for tissue architecture mapping.
An agent that interprets spatial transcriptomics data to propose mechanistic hypotheses and analyze tissue organization.
AI-powered bone marrow morphology analysis, cell classification, and hematologic disorder diagnosis using deep learning on aspirate and biopsy images.
Machine learning framework for inferring high-risk clonal hematopoiesis from complete blood count data without sequencing, reducing the number needed to sequence for CHIP screening.
AI-powered clonal hematopoiesis of indeterminate potential (CHIP) detection, risk stratification, and cardiovascular/malignancy risk prediction using genomic and clinical data.
AI-powered analysis of coagulation disorders, thrombosis risk prediction, anticoagulation management, and platelet function assessment using machine learning.
AI-powered analysis of hemoglobin disorders including sickle cell disease, thalassemias, and variant hemoglobins using HPLC, electrophoresis, and molecular data.
AI-powered myeloproliferative neoplasm monitoring for disease progression prediction, treatment response tracking, and transformation risk assessment in PV, ET, and myelofibrosis.
AI-powered minimal residual disease (MRD) analysis for multiple myeloma using next-generation flow cytometry, NGS, and mass spectrometry approaches.
AI-powered design of armored CAR-T cells with cytokine/chemokine expression for enhanced solid tumor efficacy, including IL-12, IL-15, IL-18, and IL-7 armoring strategies.
AI-guided CAR-T cell design for solid tumors using antigen prioritization, safety-by-design architectures, and exhaustion-resistant engineering.
AI-powered cytokine release syndrome (CRS) and cytokine storm analysis for prediction, monitoring, and management in immunotherapy and infectious disease.
AI-powered analysis for predicting optimal immune checkpoint inhibitor combinations based on tumor microenvironment, biomarkers, and molecular profiling.
AI-powered NK cell therapy design for cancer immunotherapy including CAR-NK engineering, memory-like NK generation, and KIR/HLA matching optimization.
AI-powered analysis of T-cell exhaustion states, epigenetic scarring, stem-like T-cell populations, and checkpoint blockade response prediction in cancer immunotherapy.
AI-powered TCR-peptide-MHC interaction prediction using AlphaFold3 and deep learning for therapeutic TCR discovery, neoantigen validation, and T cell immunogenicity assessment.
AI-powered T-cell receptor repertoire analysis for cancer diagnosis, immunotherapy response prediction, and therapeutic TCR selection using deep learning and multi-layer ML approaches.
Comprehensive AI-powered tumor microenvironment immune profiling integrating bulk deconvolution, single-cell analysis, and spatial transcriptomics for immunotherapy biomarker discovery.
Generates executable Python protocols for Opentrons OT-2 and Flex robots from natural language descriptions.
AI-powered analysis of cellular senescence for aging research, cancer therapy response, and senolytic drug development.
Tensor Operations
Bayesian Optimize
AI-powered analysis of microbiome-cancer interactions including tumor microbiome profiling, immunotherapy response prediction, and microbiome-targeted therapeutic opportunities.
AI-powered analysis of cancer metabolic reprogramming including Warburg effect, glutamine addiction, lipid metabolism, and metabolic vulnerabilities for therapeutic targeting.
AI-powered analysis of chromosomal instability (CIN) signatures for cancer prognosis, immunotherapy response prediction, and therapeutic vulnerability identification.
AI-powered circulating tumor DNA dynamics analysis for molecular residual disease detection, treatment response monitoring, and early relapse prediction using liquid biopsy.
AI-powered extracellular vesicle and exosome analysis for cancer biomarker discovery, liquid biopsy applications, and intercellular communication profiling.
AI-powered homologous recombination deficiency (HRD) analysis for PARP inhibitor response prediction using genomic scarring signatures and BRCA pathway assessment.
Ultra-sensitive AI-powered molecular residual disease detection using MRD-EDGE deep learning for sub-0.001% VAF ctDNA detection and early relapse prediction.
AI-powered analysis of patient-derived organoid (PDO) drug screening for personalized oncology treatment selection and biomarker discovery.
AI-powered pan-cancer analysis integrating genomic, transcriptomic, proteomic, and epigenomic data for cancer subtyping, driver identification, and cross-cancer pattern discovery.
AI-powered analysis of patient-derived xenograft (PDX) models for drug response prediction, translational research, and personalized treatment selection.
AI-powered multimodal fusion of radiology (CT/MRI/PET) and pathology (H&E/IHC) imaging with clinical and genomic data for comprehensive cancer diagnostics and treatment prediction.
AI-powered analysis of tumor clonal architecture, subclonal dynamics, and evolutionary trajectories from multi-region sequencing and longitudinal liquid biopsy data.
AI-powered intratumor heterogeneity analysis for clonal architecture reconstruction, subclonal evolution tracking, and therapy resistance prediction using multi-region and longitudinal sequencing.
Checks for potential drug-drug interactions (DDIs) between a list of medications.
Automates the drafting of regulatory documents (e.g., FDA CTD sections) with citation management and audit trails.
AI-powered multi-ancestry polygenic risk score calculation and optimization for equitable disease risk prediction across diverse global populations.