mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- - 11페이지

prs-net-deep-learning-agent

Geometric deep learning-based polygenic risk score prediction using PRS-Net for modeling gene interactions, enhanced disease prediction, and cross-ancestry portability.

원문 언어: 영어

deep-visual-proteomics-agent

AI-driven integration of cellular imaging, laser microdissection, and ultra-sensitive mass spectrometry for spatially-resolved single-cell proteomics.

원문 언어: 영어

core-python-best-practices

Essential guidelines for writing modern, type-safe, and idiomatic Python 3 code.

원문 언어: 영어

pandas-best-practices

Standards for efficient, readable, and performant data manipulation using Python's Pandas library.

원문 언어: 영어

nextjs-best-practices

Guidelines for building scalable, SEO-friendly applications with Next.js (App Router).

원문 언어: 영어

react-best-practices

A comprehensive guide and rule set for writing clean, performant, and maintainable React code.

원문 언어: 영어

cryoem-ai-drug-design-agent

AI-powered integration of cryo-EM structural data with generative AI and molecular dynamics for structure-based drug design targeting flexible proteins and membrane complexes.

원문 언어: 영어

time-resolved-cryoem-agent

AI-powered time-resolved cryo-EM analysis for capturing protein dynamics, drug-binding kinetics, and conformational transitions for dynamics-based drug discovery.

원문 언어: 영어

deep-research-swarm

Multi-agent research literature analysis

원문 언어: 영어

bio-single-cell-clustering

scRNA-seq clustering analysis

원문 언어: 영어

biokernel

Biomedical OS Core & MCP Server

원문 언어: 영어

meta-prompter

Automatic prompt engineering & optimization

원문 언어: 영어

nano-banana

AI-powered reasoning image engine for generating and editing high-quality biomedical infographics and realistic images.

원문 언어: 영어

paper-banana

Agentic framework for automating the generation of publication-ready academic illustrations and statistical plots.

원문 언어: 영어

clinical-trial-protocol-skill

Generate clinical trial protocols for medical devices or drugs. This skill should be used when users say "Create a clinical trial protocol", "Generate protocol for [device/drug]", "Help me design a clinical study", "Research similar trials for…

원문 언어: 영어

bio-hi-c-analysis-compartment-analysis

Detect A/B compartments from Hi-C data using cooltools and eigenvector decomposition. Identify active (A) and inactive (B) chromatin compartments from contact matrices. Use when identifying A/B compartments from Hi-C data.

원문 언어: 영어

bio-hi-c-analysis-contact-pairs

Process Hi-C read pairs using pairtools. Parse alignments, filter duplicates, classify pairs, and generate contact statistics from Hi-C sequencing data. Use when processing raw Hi-C read pairs.

원문 언어: 영어

bio-hi-c-analysis-hic-data-io

Load, convert, and manipulate Hi-C contact matrices using cooler format. Read .cool/.mcool files, convert from .hic format, access matrix data, and export to different formats. Use when loading or converting Hi-C contact matrices.

원문 언어: 영어

bio-hi-c-analysis-hic-differential

Compare Hi-C contact matrices between conditions to identify differential chromatin interactions. Compute log2 fold changes, statistical significance, and visualize differential contact maps. Use when comparing Hi-C contacts between conditions.

원문 언어: 영어

bio-hi-c-analysis-hic-visualization

Visualize Hi-C contact matrices, TADs, loops, and genomic features using matplotlib, cooltools, and HiCExplorer. Create triangle plots, virtual 4C, and multi-track figures. Use when visualizing contact matrices or genomic features.

원문 언어: 영어

bio-hi-c-analysis-loop-calling

Detect chromatin loops and point interactions from Hi-C data using cooltools, chromosight, and HiCCUPS-like methods. Identify CTCF-mediated loops and enhancer-promoter contacts. Use when detecting chromatin loops from Hi-C data.

원문 언어: 영어

bio-hi-c-analysis-matrix-operations

Balance, normalize, and transform Hi-C contact matrices using cooler and cooltools. Apply iterative correction (ICE), compute expected values, and generate observed/expected matrices. Use when normalizing or transforming Hi-C matrices.

원문 언어: 영어

bio-hi-c-analysis-tad-detection

Call topologically associating domains (TADs) from Hi-C data using insulation score, HiCExplorer, and other methods. Identify domain boundaries and hierarchical domain structure. Use when calling TADs from Hi-C insulation scores.

원문 언어: 영어

bio-clinical-databases-clinvar-lookup

Query ClinVar for variant pathogenicity classifications, review status, and disease associations via REST API or local VCF. Use when determining clinical significance of variants for diagnostic or research purposes.

원문 언어: 영어

bio-clinical-databases-dbsnp-queries

Query dbSNP for rsID lookups, variant annotations, and cross-references to other databases. Use when mapping between rsIDs and genomic coordinates or retrieving basic variant information.

원문 언어: 영어

bio-clinical-databases-gnomad-frequencies

Query gnomAD for population allele frequencies to assess variant rarity. Use when filtering variants by population frequency for rare disease analysis or determining if a variant is common in the general population.

원문 언어: 영어

bio-clinical-databases-hla-typing

Call HLA alleles from NGS data using OptiType, HLA-HD, or arcasHLA for immunogenomics applications. Use when determining HLA genotype for transplant matching, neoantigen prediction, or pharmacogenomic screening.

원문 언어: 영어

bio-clinical-databases-myvariant-queries

Query myvariant.info API for aggregated variant annotations from multiple databases (ClinVar, gnomAD, dbSNP, COSMIC, etc.) in a single request. Use when annotating variants with clinical and population data from multiple sources simultaneously.

원문 언어: 영어

bio-clinical-databases-pharmacogenomics

Query PharmGKB and CPIC for drug-gene interactions, pharmacogenomic annotations, and dosing guidelines. Use when predicting drug response from genetic variants or implementing clinical pharmacogenomics.

원문 언어: 영어

bio-clinical-databases-polygenic-risk

Calculate polygenic risk scores using PRSice-2, LDpred2, or PRS-CS from GWAS summary statistics. Use when predicting disease risk from genome-wide genetic variants.

원문 언어: 영어

bio-clinical-databases-somatic-signatures

Extract and analyze mutational signatures from somatic variants using SigProfiler or MutationalPatterns to characterize mutagenic processes. Use when identifying DNA damage mechanisms or etiology in cancer genomes.

원문 언어: 영어

bio-clinical-databases-tumor-mutational-burden

Calculate tumor mutational burden from panel or WES data with proper normalization and clinical thresholds. Use when assessing immunotherapy eligibility or characterizing tumor immunogenicity.

원문 언어: 영어

bio-clinical-databases-variant-prioritization

Filter and prioritize variants by pathogenicity, population frequency, and clinical evidence for rare disease analysis. Use when identifying candidate disease-causing variants from exome or genome sequencing.

원문 언어: 영어

bio-data-visualization-circos-plots

Create circular genome visualizations with Circos and pyCircos. Display multi-track data including ideograms, genes, variants, CNVs, and interaction arcs. Use when creating circular genome visualizations.

원문 언어: 영어

bio-data-visualization-color-palettes

Select and apply colorblind-friendly palettes for scientific figures using viridis, RColorBrewer, and custom color schemes. Use when selecting colorblind-friendly palettes for figures.

원문 언어: 영어

bio-data-visualization-genome-browser-tracks

Generate genome browser visualizations using pyGenomeTracks or IGV batch scripting for publication figures. Use when creating publication figures of genomic regions with multiple data tracks.

원문 언어: 영어

bio-data-visualization-genome-tracks

Create genome browser-style visualizations showing multiple data tracks (coverage, peaks, genes) using pyGenomeTracks, Gviz, and IGV. Use when visualizing genomic data at specific loci with multiple aligned tracks.

원문 언어: 영어

bio-data-visualization-ggplot2-fundamentals

Create publication-quality scientific figures with ggplot2 including scatter plots, boxplots, heatmaps, and multi-panel layouts. Use when creating static figures for papers, presentations, or reports in R.

원문 언어: 영어

bio-data-visualization-heatmaps-clustering

Create clustered heatmaps with row/column annotations using ComplexHeatmap, pheatmap, and seaborn for gene expression and omics data visualization. Use when visualizing expression patterns across samples or identifying co-expressed gene clusters.

원문 언어: 영어

bio-data-visualization-interactive-visualization

Create interactive HTML plots with plotly and bokeh for exploratory data analysis and web-based sharing of omics visualizations. Use when building zoomable, hoverable plots for data exploration or web dashboards.

원문 언어: 영어