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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 40 件を表示しています。
Design and operate browser and desktop agents that rely on screenshots, mouse and keyboard control, or hybrid bash/editor/computer loops. Use when deciding between DOM automation, browser agents, and full computer-use workflows.
原文の言語: 英語
Implement and operate Azure AI Foundry and Microsoft Foundry workloads with explicit identity, deployment, model versioning, safety, and agent-service controls. Use when deploying model endpoints, migrating model versions, or setting up production guardrails…
原文の言語: 英語
Integrate and operate Cohere APIs with current model, rerank, embedding, transcribe, and SDK guidance. Use when selecting Cohere models, building search or agent workflows, or planning migration across Cohere platform updates.
原文の言語: 英語
Build, evaluate, and deploy agents with Google's Agent Development Kit (ADK). Use when you want code-first multi-agent systems, workflow agents, MCP tools, or Google-supported agent deployment paths.
原文の言語: 英語
Build typed, provider-agnostic agents with PydanticAI. Use when structured I/O, dependency injection, MCP support, and OpenTelemetry-friendly observability matter more than framework hype.
原文の言語: 英語
Integrate and operate DeepSeek APIs with current docs and compatibility guidance. Use when implementing DeepSeek chat, reasoning, tool calling, or FIM workflows through its OpenAI-compatible API.
原文の言語: 英語
Integrate and operate Mistral APIs with current model catalog, SDKs, and agent features. Use when implementing Mistral chat, agents, conversations, files, or coding workflows.
原文の言語: 英語
Integrate and operate xAI Grok APIs with current documentation and SDK guidance. Use when implementing Grok tool use, Responses-style workflows, files or collections search, or migration from other provider SDKs.
原文の言語: 英語
Deploy AgentScope + AgentScope Runtime for secure sandboxed multi-agent services inside BioKernel.
原文の言語: 英語
Run OpenHands headless CLI/SDK missions from BioKernel swarms for autonomous software work.
原文の言語: 英語
Parse scholarly articles (PDF, DOI, URL) to extract metadata, GEO accessions, and acquisition links using OpenAlex + GROBID pipelines.
原文の言語: 英語
Cell type annotation for spatial transcriptomics data using marker-based scoring, Tangram mapping, scANVI transfer, or CellAssign probabilistic models.
原文の言語: 英語
Copy number variation inference from spatial transcriptomics expression data.
原文の言語: 英語
Cell-cell communication analysis via ligand-receptor interaction scoring using LIANA, CellPhoneDB, FastCCC, or CellChat.
原文の言語: 英語
Experimental condition comparison using pseudobulk differential expression with proper multi-sample statistics.
原文の言語: 英語
Differential expression analysis — find marker genes for clusters or compare two groups. Supports Wilcoxon rank-sum, t-test, and PyDESeq2 methods with publication-ready figures and CSV tables.
原文の言語: 英語
Cell type deconvolution for spatial transcriptomics — estimates per-spot cell type proportions using FlashDeconv, Cell2Location, RCTD, DestVI, Stereoscope, Tangram, SPOTlight, or CARD.
原文の言語: 英語
Identify tissue regions and spatial niches from preprocessed spatial transcriptomics data using Leiden, Louvain, SpaGCN, STAGATE, GraphST, or BANKSY.
原文の言語: 英語
Pathway and gene set enrichment analysis for spatial transcriptomics data.
原文の言語: 英語
Find genes with spatially variable expression patterns using Moran's I, SpatialDE, SPARK-X, or FlashS. Identifies genes whose expression is non-randomly distributed across tissue coordinates.
原文の言語: 英語
Multi-sample integration and batch correction for spatial transcriptomics data.
原文の言語: 英語
Load spatial transcriptomics data (Visium, Xenium, MERFISH, Slide-seq, generic h5ad), perform QC filtering, normalization, HVG selection, PCA, UMAP, and Leiden clustering.
原文の言語: 英語
Spatial registration and multi-slice alignment for spatial transcriptomics data.
原文の言語: 英語
Comprehensive spatial statistics toolkit — cluster-level (neighborhood enrichment, Ripley, co-occurrence), gene-level (Moran's I, Geary's C, local Moran, Getis-Ord), and network-level analysis.
原文の言語: 英語
Trajectory inference and pseudotime analysis for spatial transcriptomics data.
原文の言語: 英語
RNA velocity and cellular dynamics analysis for spatial transcriptomics data.
原文の言語: 英語
Bulk RNA-seq count matrix QC — library size, gene detection rates, and sample correlation.
原文の言語: 英語
WGCNA-style weighted gene co-expression network analysis — module detection, soft thresholding, hub genes.
原文の言語: 英語
Bulk RNA-seq differential expression analysis using PyDESeq2 with optional edgeR/limma-voom via rpy2.
原文の言語: 英語
Bulk RNA-seq cell type deconvolution using NNLS (built-in), with optional CIBERSORTx and MuSiC bridges.
原文の言語: 英語
Pathway enrichment analysis for bulk RNA-seq — ORA and GSEA via GSEApy, with built-in hypergeometric fallback.
原文の言語: 英語
Alternative splicing analysis — PSI quantification, differential splicing event detection from rMATS/SUPPA2 output.
原文の言語: 英語
Batch integration for multi-sample scRNA-seq using Harmony, scVI, Seurat CCA/RPCA, BBKNN, and fastMNN. Remove technical variation while preserving biological differences.
原文の言語: 英語
Automated cell type annotation using marker genes, CellTypist, SingleR, or scmap. Supports custom references and marker gene lists.
原文の言語: 英語
Cell-cell communication analysis via ligand-receptor interaction scoring using CellChat (R), NicheNet (R), LIANA (Python), or built-in L-R database.
原文の言語: 英語
Differential expression analysis for single-cell data — marker gene discovery using Wilcoxon, t-test, MAST, or DESeq2 pseudo-bulk analysis.
原文の言語: 英語
Doublet detection and removal using Scrublet (Python), DoubletFinder (R), and scDblFinder (R). Essential QC step before clustering.
原文の言語: 英語
Gene regulatory network inference using pySCENIC three-step pipeline (GRNBoost2 → cisTarget → AUCell), with correlation-based fallback. Identifies transcription factor regulons and scores their activity per cell.
原文の言語: 英語
Multi-omics integration for single-cell data (CITE-seq, 10X Multiome, SHARE-seq). Weighted Nearest Neighbor (WNN) analysis, MOFA+, and muon/MuData workflows.
原文の言語: 英語
Single-cell RNA-seq QC, normalization, HVG selection, PCA, UMAP, and Leiden clustering. Supports both Scanpy (Python) and Seurat (R) workflows.
原文の言語: 英語