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mims-harvard/ToolUniverse - 7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
その他の生物科学者
説明

Retrieve DNA/RNA/protein sequences from NCBI and ENA with disambiguation. Quality hierarchy: RefSeq (NM_/NP_) > RefSeq predicted (XM_/XP_) > GenBank submissions. Use for fetching specific sequences by accession, gene-symbol-to-sequence lookup,…

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

Spatial multi-omics interpretation pipeline. Transforms spatially variable genes (SVGs), domain annotations, and tissue context into biological insights via domain-by-domain characterization, cell-type composition, spatial gene expression patterns,…

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

Structural variant (SV) clinical interpretation: deletions, duplications, inversions, translocations, complex rearrangements. Applies ACMG-adapted criteria with ClinGen HI/TS dosage scores, gnomAD frequencies, and ClinVar evidence. Produces 5-tier…

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

Computational vaccine candidate design: peptide/subunit vaccines via MHC-I/MHC-II epitope prediction (IEDB), population HLA coverage optimization, B-cell epitope identification, and cross-strain conservation analysis. Use for vaccine epitope prediction, HLA…

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

Create high-quality ToolUniverse skills following test-driven, implementation-agnostic methodology.

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

Optimize ToolUniverse skills for better report quality, evidence handling, and user experience. Apply patterns like tool verification, foundation data layers, disambiguation-first, evidence grading, quantified completeness, and report-only output. Use when…

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

Orchestrate the full ToolUniverse self-improvement cycle: discover APIs, create tools, test with researcher personas, fix issues, optimize skills, and push via git. References and dispatches to all other devtu skills. Use when asked to: run the…

原文の言語: 英語

更新
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

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
疫学者
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
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

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

更新
職業分類
その他の生物科学者
説明

Spatial multi-omics interpretation pipeline. Transforms spatially variable genes (SVGs), domain annotations, and tissue context into biological insights via domain-by-domain characterization, cell-type composition, spatial gene expression patterns,…

原文の言語: 英語

更新
職業分類
医学科学者(疫学者除く)
説明

Dose-response / concentration-response curve fitting — IC50, EC50, Hill slope, Emax/Emin efficacy, and relative potency from paired concentration vs response data (enzyme/cell assays, drug screening, agonist/antagonist pharmacology). Fits the 4-parameter…

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-combination synergy analysis — quantify whether two drugs together are synergistic, additive, or antagonistic using the standard reference models (Bliss independence, HSA / highest single agent, Loewe additivity, ZIP, and the Chou-Talalay Combination…

原文の言語: 英語

更新
職業分類
生化学者・生物物理学者
説明

Enzyme kinetics — Michaelis-Menten Km, Vmax, kcat (turnover), and kcat/Km (catalytic efficiency / specificity constant) from substrate-velocity data, plus inhibition-mechanism analysis (competitive / uncompetitive / non-competitive, Ki). Fits the MM equation…

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
生物技術者
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

更新
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

更新
職業分類
その他の生物科学者
説明

PCR / qPCR primer and oligo design — design forward/reverse primers for a target region (SantaLucia nearest-neighbor thermodynamics), compute melting temperature (Tm) and annealing temperature (Ta), check GC content, and screen an oligo for hairpins and…

原文の言語: 英語

更新
職業分類
医学科学者(疫学者除く)
説明

Dose-response / concentration-response curve fitting — IC50, EC50, Hill slope, Emax/Emin efficacy, and relative potency from paired concentration vs response data (enzyme/cell assays, drug screening, agonist/antagonist pharmacology). Fits the 4-parameter…

原文の言語: 英語

更新
職業分類
医学科学者(疫学者除く)
説明

Drug-combination synergy analysis — quantify whether two drugs together are synergistic, additive, or antagonistic using the standard reference models (Bliss independence, HSA / highest single agent, Loewe additivity, ZIP, and the Chou-Talalay Combination…

原文の言語: 英語

更新
職業分類
生化学者・生物物理学者
説明

Enzyme kinetics — Michaelis-Menten Km, Vmax, kcat (turnover), and kcat/Km (catalytic efficiency / specificity constant) from substrate-velocity data, plus inhibition-mechanism analysis (competitive / uncompetitive / non-competitive, Ki). Fits the MM equation…

原文の言語: 英語

更新
職業分類
疫学者
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

更新
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

更新
職業分類
その他の生物科学者
説明

PCR / qPCR primer and oligo design — design forward/reverse primers for a target region (SantaLucia nearest-neighbor thermodynamics), compute melting temperature (Tm) and annealing temperature (Ta), check GC content, and screen an oligo for hairpins and…

原文の言語: 英語

更新
職業分類
その他の生物科学者
説明

Universal data access patterns for downloading and parsing scientific data when ToolUniverse tools don't cover the source, only return metadata, or you need bulk records. Use for VCF/h5ad/BAM/SDF/GCT parsing, multi-step API workflows (search to filter to…

原文の言語: 英語

更新
職業分類
医学科学者(疫学者除く)
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

更新
職業分類
医学科学者(疫学者除く)
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

更新
職業分類
ソフトウェア開発者
説明

Add custom local tools to ToolUniverse alongside the 1000+ built-in tools. Covers JSON-config tools (simplest, no code), Python class tools (REST/SOAP/GraphQL APIs, computational logic), and best-practices for return schemas. Use for wrapping new APIs, adding…

原文の言語: 英語

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職業分類
生化学者・生物物理学者
説明

Given a set of residues in a protein, explain WHY they are functionally critical by combining structural context (binding interface, ligand pocket, core, secondary structure), UniProt features (active sites, binding sites, PTM sites, disulfides), optional SAE…

原文の言語: 英語

更新
職業分類
医学科学者(疫学者除く)
説明

Clinical interpretation of somatic cancer mutations for precision oncology. Transforms a gene + variant + cancer-type input into an actionable report: clinical evidence tier (CIViC, OncoKB), therapeutic options (FDA-approved + investigational), resistance…

原文の言語: 英語

更新
職業分類
その他の生物科学者
説明

Universal data access patterns for downloading and parsing scientific data when ToolUniverse tools don't cover the source, only return metadata, or you need bulk records. Use for VCF/h5ad/BAM/SDF/GCT parsing, multi-step API workflows (search to filter to…

原文の言語: 英語

更新
職業分類
医学科学者(疫学者除く)
説明

Quantitative drug-target validation pipeline. Scores druggability, selectivity, safety profile, ADMET feasibility, and structural tractability with a composite Target Validation Score (0-100) and GO/NO-GO recommendation. Use for go/no-go decisions on a target…

原文の言語: 英語

更新
職業分類
その他の生物科学者
説明

Systems biology and pathway analysis integrating Reactome, KEGG, WikiPathways, BioCarta, NCI-Nature Pathway Interaction Database. Multi-database pathway enrichment, protein-pathway relationships, network reasoning. Use for pathway analysis on a gene list,…

原文の言語: 英語

更新
職業分類
医学科学者(疫学者除く)
説明

Quantitative drug-target validation pipeline. Scores druggability, selectivity, safety profile, ADMET feasibility, and structural tractability with a composite Target Validation Score (0-100) and GO/NO-GO recommendation. Use for go/no-go decisions on a target…

原文の言語: 英語

更新
職業分類
微生物学者
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

更新
職業分類
その他の生物科学者
説明

Stem cell, iPSC, and organoid research — pluripotency markers, differentiation protocol pathways, lineage commitment factors, organoid model selection. Use for iPSC characterization, differentiation protocol design via developmental-pathway recapitulation,…

原文の言語: 英語

更新
職業分類
その他の生物科学者
説明

Systems biology and pathway analysis integrating Reactome, KEGG, WikiPathways, BioCarta, NCI-Nature Pathway Interaction Database. Multi-database pathway enrichment, protein-pathway relationships, network reasoning. Use for pathway analysis on a gene list,…

原文の言語: 英語

更新
職業分類
医学科学者(疫学者除く)
説明

Clinical interpretation of somatic cancer mutations for precision oncology. Transforms a gene + variant + cancer-type input into an actionable report: clinical evidence tier (CIViC, OncoKB), therapeutic options (FDA-approved + investigational), resistance…

原文の言語: 英語

更新
収集済み skill 339 件中 40 件を表示しています。