スキルを探す

Query the ZINC22 virtual compound library (CartBlanche API, billions of make-on-demand + purchasable molecules). Look up substances by ZINC ID, resolve a SMILES to its ZINC ID (exact match), inspect purchasability/catalogs, and assemble compound sets for docking. Property (MW/logP) filtering is done locally with RDKit. For bioactivity use chembl-database-bioactivity; for approved drugs use drugbank-database-access.

日本語の概要は準備中です。原文の説明を表示しています。

Query Reactome pathways via REST: pathway queries, entity lookup, keyword search, gene list enrichment, hierarchy, cross-refs. Content + Analysis services. Python wrapper: reactome2py. For KEGG use kegg-database; for PPIs use string-database-ppi.

日本語の概要は準備中です。原文の説明を表示しています。

Read/write SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ. Region queries, pileup, variant filtering, read groups. Python htslib wrapper exposing samtools/bcftools CLI. Use STAR/BWA for alignment; GATK/DeepVariant for variant calling.

日本語の概要は準備中です。原文の説明を表示しています。

Evaluating scientific evidence and claims. Covers study design hierarchy (RCT to expert opinion), effect sizes (OR, RR, NNT, Cohen's d), confounding, p-value vs clinical significance, GRADE quality assessment, reproducibility, and bias types (selection, information, confounding, reporting). Use when reading a paper or assessing claims.

日本語の概要は準備中です。原文の説明を表示しています。

Systematic strategies for searching scientific literature across PubMed, arXiv, Google Scholar, and AI-assisted tools. Covers PICO framework for clinical questions, three-tiered search (database-specific, AI-assisted, content extraction), PubMed field tags and MeSH, boolean query construction, and full-text extraction. Use when planning a literature search or choosing a search tier.

日本語の概要は準備中です。原文の説明を表示しています。

Query ChEMBL (2M+ compounds, 19M+ bioactivity measurements, 13K+ targets) via the public REST/JSON API with plain `requests` — no SDK install required. Search compounds, retrieve IC50/Ki/EC50 bioactivities, find target inhibitors, run SAR, access drug mechanism/indication data.

日本語の概要は準備中です。原文の説明を表示しています。

Query FDA drug labels (DailyMed) via REST API. Search structured product labels (SPLs) by name, NDC, set ID, or RxCUI; get indications, dosage, warnings, adverse reactions, packaging. No auth. For adverse events use fda-database; for DDIs use ddinter-database.

日本語の概要は準備中です。原文の説明を表示しています。

Parse local DrugBank XML for drug info, interactions, targets, and properties. Search by ID/name/CAS, extract DDIs with severity, map targets/enzymes/transporters, compute SMILES similarity. Primary via local XML; REST API rate-limited (3k/month dev). For live bioactivity use chembl-database-bioactivity; for compound properties use pubchem-compound-search.

日本語の概要は準備中です。原文の説明を表示しています。

Look up EMDB cryo-EM density maps and fitted atomic models via the entry REST API + EBI Search WS. Fetch entry metadata (resolution, method, organism, sample), map download URLs, fitted PDB IDs, and citations. Keyword search via EBI Search. No auth. For atomic coordinates use pdb-database; for AlphaFold predictions use alphafold-database-access.

日本語の概要は準備中です。原文の説明を表示しています。

Query PubChem (110M+ compounds) directly via the PUG-REST/JSON API with plain `requests` — no SDK install required. Search by name/CID/SMILES/InChIKey/formula, retrieve properties (MW, XLogP, TPSA, H-bond counts), do similarity/substructure searches with async ListKey polling, fetch synonyms, descriptions, assay summaries, and download SDF/PNG. For local cheminformatics use rdkit; for bioactivity-centric workflows use chembl-database-bioactivity.

日本語の概要は準備中です。原文の説明を表示しています。

Open-source FAIR biology data framework. Version artifacts (AnnData, DataFrame, Zarr), track lineage, validate via ontologies (Bionty), query datasets. Integrates with Nextflow, Snakemake, W&B, scVI. For scRNA-seq use scanpy; for ontology lookups use bionty.

日本語の概要は準備中です。原文の説明を表示しています。

Multi-Omics Factor Analysis v2 (MOFA+) with mofapy2. Jointly decompose omics layers (scRNA, ATAC, proteomics, methylation) into latent factors capturing major variation. Multi-group designs. AnnData views → MOFA object → train → variance explained → correlate factors with metadata → visualize/cluster → enrich top loadings.

日本語の概要は準備中です。原文の説明を表示しています。

Python framework for single- and multi-objective optimization with evolutionary algorithms. Define vectorized objectives and constraints; solve with NSGA-II, NSGA-III, MOEA/D, GAs, or differential evolution. Analyze Pareto fronts, visualize trade-offs, customize operators and callbacks. For engineering design, hyperparameter search, and conflicting objectives. Alternatives: scipy.optimize (single-objective, gradient), platypus, jMetalPy (Java).

日本語の概要は準備中です。原文の説明を表示しています。

Structured hypothesis formulation: turn observations into testable hypotheses with predictions, propose mechanisms, design experiments. Follows the scientific method. Use scientific-brainstorming for open ideation; hypogenic for automated LLM hypothesis testing on datasets.

日本語の概要は準備中です。原文の説明を表示しています。

The Lancet figure preparation: resolution (300+ DPI at 120%), preferred editable formats (PowerPoint/Word/SVG), column widths (75/154 mm), Times New Roman, in-house redraw policy.

日本語の概要は準備中です。原文の説明を表示しています。

Research posters in LaTeX using beamerposter, tikzposter, or baposter. Layout, typography, color schemes, figure integration, accessibility, and QA for conferences. Includes templates. For figure generation use matplotlib-scientific-plotting or plotly-interactive-plots.

日本語の概要は準備中です。原文の説明を表示しています。

Conducting systematic, scoping, and narrative literature reviews. Covers PRISMA/PRISMA-ScR protocols, search strategy (Boolean, MeSH), database selection (PubMed, Scopus, Web of Science, Embase), screening, data extraction, evidence synthesis (narrative, meta-analysis, thematic), and reporting. Use when planning or executing a formal literature review.

日本語の概要は準備中です。原文の説明を表示しています。

NEJM figure preparation: resolution (300-1200 DPI), editable vector formats (AI/EPS/SVG), in-house medical illustration policy, and strict image integrity requirements.

日本語の概要は準備中です。原文の説明を表示しています。

Query OpenAlex REST API for 250M+ scholarly works, authors, institutions, journals, concepts. Search by keyword, author, DOI, ORCID, or ID; filter by year, OA, citations, field; retrieve citations, references, author disambiguation. Free, no auth. For PubMed use pubmed-database; preprints use biorxiv-database.

日本語の概要は準備中です。原文の説明を表示しています。

Structured peer review of manuscripts and grants. 7-stage evaluation: initial assessment, section review, statistical rigor, reproducibility, figure integrity, ethics, writing. Covers CONSORT/STROBE/PRISMA and report structure. For evidence quality see scientific-critical-thinking; scoring see scholar-evaluation.

日本語の概要は準備中です。原文の説明を表示しています。

PNAS figure preparation: resolution (300-1000 PPI), formats (TIFF/EPS/PDF), strict RGB-only color, Arial/Helvetica fonts, italicized uppercase panel labels, automated image screening.

日本語の概要は準備中です。原文の説明を表示しています。

Programmatic PubMed access via NCBI E-utilities REST API. Covers Boolean/MeSH queries, field-tagged search, endpoints (ESearch, EFetch, ESummary, EPost, ELink), history server for batches, citation matching, systematic review strategies. Use for biomedical literature search or automated pipelines.

日本語の概要は準備中です。原文の説明を表示しています。

Science (AAAS) figure preparation: resolution (150-300+ DPI), formats (PDF/EPS/TIFF), RGB color, Myriad/Helvetica fonts, strict image manipulation policies including gamma adjustment disclosure.

日本語の概要は準備中です。原文の説明を表示しています。

Query bioRxiv/medRxiv preprints via REST API. Search by DOI, category, or date range; retrieve metadata (title, abstract, authors, category, DOI, version history) and PDFs. No auth. For peer-reviewed biomedical use pubmed-database; broader scholarly search use openalex-database.

日本語の概要は準備中です。原文の説明を表示しています。