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mims-harvard/ToolUniverse - 4ページ

SkillsMP は mims-harvard/ToolUniverse から 339 件の skill を収集しています。skill を開くとソースと詳細を確認できます。

mims-harvard/ToolUniverse

収集済み skill 339 件中 40 件を表示しています。

職業分類
医学科学者(疫学者除く)
説明

Transform GWAS signals into drug targets and repurposing opportunities. Connects GWAS-significant loci to causal genes via fine-mapping/eQTL, then to druggable proteins via DGIdb/OpenTargets, then to existing drugs via ChEMBL. Use for GWAS-to-target…

原文の言語: 英語

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職業分類
その他の生物科学者
説明

Statistical fine-mapping of GWAS loci using credible sets (SuSiE, FINEMAP) and locus-to-gene scoring (Open Targets L2G). Identifies likely causal variants and target genes — distinct from positional 'nearest gene' which is often wrong. Use for prioritizing…

原文の言語: 英語

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職業分類
その他の生物科学者
説明

Interpret a single GWAS SNP across multiple databases — GWAS Catalog hits, LD/haplotype context, eQTL evidence, regulatory annotation, ClinVar pathogenicity, gnomAD frequency. Use for 'what does this SNP do', SNP-to-mechanism tracing, and resolving…

原文の言語: 英語

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職業分類
その他の生物科学者
説明

Compare GWAS studies, perform meta-analyses across cohorts, and assess signal replication. Uses GWAS Catalog metadata, study-level statistics, and cross-cohort comparison. Use for evaluating GWAS reproducibility for a trait, meta-analysis sample size and…

原文の言語: 英語

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職業分類
その他の生物科学者
説明

Discover causal genes for diseases/traits from GWAS data using Open Targets L2G (locus-to-gene) scoring — integrates eQTL, chromatin interaction, and distance evidence. Use for trait-to-gene mapping, drug-target hypothesis generation from GWAS, and replacing…

原文の言語: 英語

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職業分類
医学科学者(疫学者除く)
説明

HLA gene-family analysis and MHC-peptide binding for transplant compatibility, vaccine epitope coverage, and cancer immunotherapy. Uses IMGT (HLA polymorphism), IEDB (epitope-MHC binding), UniProt (annotation), DGIdb (druggability). Use for HLA…

原文の言語: 英語

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職業分類
その他の生物科学者
説明

Microscopy and quantitative imaging analysis — colony morphometry, fluorescence intensity quantification, cell-count statistics, dose-response curves, and ANOVA/Dunnett on image-derived measurements. Uses pandas/numpy/scipy/scikit-image. Use for analyzing…

原文の言語: 英語

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職業分類
その他の生物科学者
説明

TCR/BCR repertoire analysis — V(D)J segment usage, CDR3 sequence diversity, clonality scoring, antigen specificity matching to IEDB, public-clone identification. Use for adaptive immune response characterization, post-treatment immune monitoring,…

原文の言語: 英語

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職業分類
医学科学者(疫学者除く)
説明

Immunology research workflows: antibody-antigen interactions, T/B cell repertoire, MHC/HLA binding prediction, autoimmune disease genetics, vaccine epitope mapping. Uses IEDB, IMGT, SAbDab, UniProt. Use for adaptive immunity questions, immune response…

原文の言語: 英語

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職業分類
医学科学者(疫学者除く)
説明

Predict patient response to immune checkpoint inhibitors (ICIs) by integrating tumor mutational burden (TMB), microsatellite instability (MSI), PD-L1 expression, HLA status, and immune-related gene expression. Outputs ICI Response Score with drug-specific…

原文の言語: 英語

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職業分類
医学科学者(疫学者除く)
説明

Rapid pathogen characterization and drug repurposing for outbreaks. Combines pathogen genomics (NCBI, BVBRC), host immune response (IEDB), drug-target databases (ChEMBL, DGIdb), and literature surveillance (PubMed/EuropePMC). Use for emerging-pathogen…

原文の言語: 英語

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職業分類
化学者
説明

Inorganic chemistry, physical chemistry, and materials science — crystal structures, coordination chemistry, lattice parameters, thermodynamic properties, electronic structure. Use for unit cell volume calculations, coordination geometry, materials property…

原文の言語: 英語

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職業分類
その他コンピュータ職
説明

Detect and auto-install missing ToolUniverse research skills. Checks common Claude Code/Cursor/Codex skill directories for the canary file, and installs any missing skills if none found. Use when the plugin's research skills aren't loading, when migrating…

原文の言語: 英語

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職業分類
その他の生物科学者
説明

KEGG-based disease-drug-variant network research. Connects diseases to causal genes, drugs to molecular targets, and variants to pathways using KEGG's editorially curated databases (KEGG Disease, Drug, Network, Variant, Pathway). Use for drug repurposing via…

原文の言語: 英語

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職業分類
その他の生物科学者
説明

Lipid analysis and lipid-disease associations using LIPID MAPS classification, HMDB metabolite data, KEGG/Reactome lipid pathways (sphingolipid, eicosanoid, steroid, fatty acid), and PubChem chemical info. Use for lipid identification, lipid metabolism…

原文の言語: 英語

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職業分類
その他の生物科学者
説明

Deep literature review — PubMed, EuropePMC, bioRxiv preprints, citation networks, evidence synthesis. Disambiguates queries, runs collision-aware searches, grades evidence T1-T4, and produces structured reports. Use for systematic literature review,…

原文の言語: 英語

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職業分類
その他の生物科学者
説明

Mendelian randomization (MR) causal inference — does an exposure, risk factor, or biomarker CAUSALLY affect a disease/outcome, using genetic variants as instrumental variables (IEU OpenGWAS / EpiGraphDB MR-EvE). Use this whenever the user asks if X causes Y,…

原文の言語: 英語

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職業分類
社会科学研究助手
説明

Meta-analysis / evidence synthesis — pool effect sizes across studies (odds ratios, risk ratios, hazard ratios, mean differences, correlations, GWAS betas) with fixed- or random-effects models, quantify heterogeneity (Q, I², τ²), and build a forest plot. Use…

原文の言語: 英語

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職業分類
その他の生物科学者
説明

Analyze metabolomics data end-to-end — metabolite identification, quantification (TIC normalization, batch correction), differential analysis, and pathway interpretation. Use for processing mass-spec metabolomics output, normalization choice, untargeted…

原文の言語: 英語

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職業分類
その他の生物科学者
説明

Metabolomics pathway analysis — metabolite identification (HMDB, KEGG, ChEBI), pathway mapping (Reactome, KEGG, MetaCyc), disease associations, enzyme/gene linkage. Use for metabolite-to-pathway-to-disease connections, BridgeDb-based ID conversion, and…

原文の言語: 英語

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職業分類
その他の生物科学者
説明

Metabolomics research — metabolite identification, study analysis, and database searches across HMDB, MetaboLights, Metabolomics Workbench, KEGG. Use for annotating mass-spec features to known metabolites, finding metabolomics studies of a disease, and…

原文の言語: 英語

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職業分類
その他の生物科学者
説明

Microbiome and metagenomics analysis using MGnify, GTDB taxonomy, ENA sequencing data, and EuropePMC literature. Covers taxonomic classification, genome quality assessment, biome-clinical phenotype linkage, and pathway interpretation. Use for amplicon/shotgun…

原文の言語: 英語

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職業分類
その他の生物科学者
説明

Genome-ASSEMBLY discovery, QC, and replicon mapping for any organism (bacteria, archaea, fungi, and beyond) using NCBI Datasets. Resolves an organism name or taxid to assemblies, picks the reference/representative or best-quality assembly, pulls assembly QC…

原文の言語: 英語

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職業分類
その他の生物科学者
説明

Microbiome research using MGnify, GTDB, ENA, OLS (ENVO biomes), and EuropePMC. Covers study discovery, taxonomic profiling, host-microbe interaction analysis, and biome-by-condition queries. Use for microbiome study selection, organism-environment…

原文の言語: 英語

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職業分類
その他の生物科学者
説明

Molecular cloning assembly design — Gibson Assembly (overlap design for seamless multi-fragment joining) and Golden Gate Assembly (Type IIS / BsaI / BbsI design with unique 4-bp fusion overhangs). Use when you need to plan how to join DNA fragments into a…

原文の言語: 英語

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職業分類
その他の生物科学者
説明

Multi-omics integration — orchestrate per-layer analysis (transcriptomics, proteomics, epigenomics, genomics, metabolomics) then perform cross-omics correlation, multi-omics clustering, and pathway-level integration. Use for integrative systems-biology…

原文の言語: 英語

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職業分類
微生物学者
説明

Dereplicate a putative natural product and assign its chemical taxonomy. Use to answer "is [compound] a known natural product", "what microbe/organism produces [compound]", "what chemical class is [compound]", "dereplicate this metabolite (by formula/exact…

原文の言語: 英語

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職業分類
その他心理士
説明

Neuroscience research workflows: neuroanatomy, neural circuits, neurotransmitter biology, neurological/psychiatric disease genetics, neural-protein function. Uses Allen Brain Atlas, WormBase (C. elegans connectome), UniProt for neural proteins, PubMed for…

原文の言語: 英語

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職業分類
その他の生物科学者
説明

Non-coding RNA analysis — miRNAs (miRBase, miRDB targets), lncRNAs (LNCipedia, RNAcentral), circRNAs, snoRNAs, and other ncRNA classes. Distinct mechanisms per class — miRNAs repress mRNA; lncRNAs scaffold/decoy/enhance. Use for ncRNA function prediction,…

原文の言語: 英語

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職業分類
化学者
説明

Organic chemistry reasoning guide for reaction product prediction, mechanism analysis (electrophilic/nucleophilic substitution, addition, elimination, pericyclic, radical), and spectroscopy interpretation (1H/13C NMR, IR, MS). Reasons from first principles…

原文の言語: 英語

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職業分類
その他の生物科学者
説明

Connect GWAS variants to biological pathways and druggable targets. Maps GWAS hits to causal genes (via fine-mapping/eQTL), then to pathways (Reactome, KEGG, WikiPathways), then to existing drugs hitting those pathways. Use for pathway-level disease…

原文の言語: 英語

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職業分類
医学科学者(疫学者除く)
説明

Pharmacogenomics (PGx) research — drug-gene interactions (CPIC, PharmGKB), CPIC dosing guidelines, variant-drug-response associations, ethnic-allele-frequency considerations, and metabolizer-status scoring. Use for PGx-informed dosing recommendations, CYP/HLA…

原文の言語: 英語

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職業分類
医学科学者(疫学者除く)
説明

Pharmacokinetic (PK) analysis of concentration-time data — non-compartmental analysis (NCA) for Cmax, Tmax, AUC (0-t and 0-∞), terminal half-life, clearance (CL), volume of distribution (Vd), MRT, and absolute bioavailability (F). Also one-compartment…

原文の言語: 英語

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職業分類
医学科学者(疫学者除く)
説明

Drug safety and adverse event analysis — FAERS spontaneous-report mining, FDA black-box warnings, signal detection (PRR, ROR, IC), risk factors by demographic/comorbidity, and label change tracking. Use for post-market safety surveillance, AE signal…

原文の言語: 英語

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職業分類
その他の生物科学者
説明

Cross-ancestry / cross-biobank phenome-wide association (PheWAS) and replication. Given ONE variant (rsID) or ONE gene, look up every phenotype it associates with across European/UK (UKB-TOPMed), Finnish (FinnGen), Japanese (BioBank Japan), and Taiwanese…

原文の言語: 英語

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職業分類
その他の生物科学者
説明

Phylogenetic analysis — de novo multiple sequence alignment (Clustal Omega/MUSCLE/MAFFT via EBI_msa_align) and neighbour-joining/UPGMA tree building (EBI_build_phylogenetic_tree) from your own sequences, plus tree analysis, treeness, saturation (PhyKIT),…

原文の言語: 英語

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職業分類
土壤・植物科学者
説明

Plant genomics and biology research — PlantReactome pathways, Ensembl Plants gene structure, POWO species taxonomy, UniProt annotation, KEGG plant pathways. Handles polyploidy (wheat hexaploidy etc.) and homeologous gene copies. Use for crop-gene annotation,…

原文の言語: 英語

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職業分類
その他の生物科学者
説明

Population genetics using the 1000 Genomes Project (IGSR) — superpopulation/population search, sample metadata, variant frequencies across AFR/AMR/EAS/EUR/SAS, ancestry-specific analyses. Use for ancestry comparison, population-aware allele frequency lookups,…

原文の言語: 英語

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職業分類
その他の生物科学者
説明

Population genetics analysis — allele frequencies (gnomAD, 1000 Genomes), Hardy-Weinberg equilibrium testing, Fst between populations, GWAS associations, evolutionary constraint scores. Use for cross-population variant comparison, ancestry-aware allele…

原文の言語: 英語

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職業分類
医学科学者(疫学者除く)
説明

Patient stratification for precision medicine — integrate genomic, clinical, and therapeutic data to split patients into responder/non-responder groups, risk tiers, or treatment-decision groups. Use for stratification-by-biomarker, treatment-selection logic,…

原文の言語: 英語

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収集済み skill 339 件中 40 件を表示しています。