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このリポジトリの skills
SkillsMP は mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- から 810 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。
mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-収集済み skill 810 件中 10 件を表示しています。
End-to-end spatial transcriptomics workflow for Visium/Xenium data. Covers data loading, preprocessing, spatial analysis, domain detection, and visualization with Squidpy. Use when analyzing spatial transcriptomics data.
原文の言語: 英語
End-to-end alternative splicing analysis from FASTQ to differential splicing results. Aligns with STAR 2-pass mode, performs junction QC, runs rMATS-turbo for differential analysis, and generates sashimi visualizations. Use when performing comprehensive…
原文の言語: 英語
End-to-end TCR/BCR repertoire analysis from FASTQ to clonotype diversity metrics. Use when analyzing immune repertoire sequencing data from bulk or single-cell experiments.
原文の言語: 英語
Structure raw clinical notes into SOAP-format summaries with explicit contradictions, missing data, and ICD-linked assessments using the provided prompt + usage script.
原文の言語: 英語
Fuse genomic variants, pathology findings, and clinical context to draft evidence-linked therapy options for tumor board review.
原文の言語: 英語
Parse trial protocols and patient data to produce criterion-level MET/NOT/UNKNOWN determinations with evidence and gaps for clinical trial screening tasks.
原文の言語: 英語
Use the AgentD workflow to mine evidence, design molecules, and rank candidates with SAR plus ADMET annotations for early drug discovery tasks.
原文の言語: 英語
Compute RDKit-driven molecular properties (MW, logP, TPSA, QED, Lipinski) for a SMILES string to support downstream drug discovery tools.
原文の言語: 英語
Execute the MAD-based single-cell RNA-seq QC workflow (scripts + Python API) to filter low-quality cells and emit reports plus filtered AnnData files.
原文の言語: 英語
Use the local Biomni checkout to orchestrate its 150+ biomedical tools, databases, and know-how workflows for complex research questions.
原文の言語: 英語