데이터 분석, 머신러닝, LLM 앱, 프롬프트 설계 (9.1천건)
Unified Python access to 40+ bioinformatics web services; use when you need to query multiple databases (e.g., UniProt/KEGG/ChEMBL/Reactome) with one consistent API in a single workflow, especially for cross-database analysis and identifier mapping.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, process FASTQ/BAM/VCF, for developing and executing genomics pipelines.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate forest plots for meta-analysis of survival data. Input is a CSV file containing study names, HR and 95% confidence intervals, output forest plot PNG and data table CSV. Supports both R and Python scripts.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate Meta-analysis funnel plots and perform publication bias testing. Takes CSV file with Meta-analysis data as input, outputs funnel plot PNG, Egger test and Begg test results.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Extracts clinical trial baseline data (study, region, participants, etc.) from article text or PMID. Checks PubMed for metadata; always falls back to LLM extraction for full details.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Decompose genetic effects into direct and indirect paths through mediating variables using the mediation R package. Tests whether gene expression, methylation, or other molecular phenotypes mediate the effect of genetic variants on disease. Use when testing whether a molecular...
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Advanced Biopython modules for motifs, population genetics, sequence utilities, restriction analysis, clustering, and GenomeDiagram visualization; use when you need extended bioinformatics analysis beyond basic sequence I/O and alignment.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
A comprehensive toolbox for computational molecular biology; use it when you need programmatic sequence/structure parsing, batch bioinformatics pipelines, or automated NCBI/BLAST workflows.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyze data with `adme-property-predictor` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Sequence alignment and alignment file processing with Biopython (Bio.Align/Bio.AlignIO), triggered when you need global/local pairwise alignment, MSA read/write/format conversion, or alignment statistics/filtering.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Detect and correct for horizontal pleiotropy in Mendelian randomization analyses using MR-PRESSO for outlier removal, MR-Egger regression for directional pleiotropy, and Steiger filtering for variant directionality. Use when validating MR results, detecting pleiotropic instrum...
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use Bio.PDB to parse and analyze protein structures (PDB/mmCIF) for structural bioinformatics tasks; use when you need structure parsing, geometry calculations, or structural comparison/superposition.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Data structure for annotated matrices in single-cell analysis; use when reading/writing .h5ad (or zarr) and exchanging data with the scverse ecosystem.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use Biopython to read/write/convert biological sequence files (FASTA/GenBank/FASTQ, etc.) and perform basic sequence operations; use when you need reliable sequence I/O, lightweight sequence manipulation, or scalable processing of large sequence datasets.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use Bio.Phylo to read/write phylogenetic trees and perform visualization and statistics; use when tree parsing/conversion, pruning/rerooting, distance calculation, or plotting is required.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate photorealistic rendering scripts for PyMOL and UCSF ChimeraX.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when creating journal cover images, generating scientific artwork prompts, or designing graphical abstracts. Creates detailed prompts for AI image generators to produce publication-quality scientific visuals.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generates complete conventional non-oncology diagnostic machine-learning research designs from a user-provided disease context, optional mechanism theme, and validation direction. Use when a study centers on disease-vs-control transcriptome comparison, optional mechanism-gene restriction, feature shrinkage, diagnostic model construction, ROC / calibration / DCA evaluation, interpretation layers, and orthogonal validation. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generates complete tumor immune-infiltration-guided bulk-transcriptome diagnostic biomarker and machine-learning research designs from a user-provided cancer type and study direction. Always use this skill whenever a user wants to design, plan, or build a tumor bioinformatics study centered on differential expression, immune infiltration estimation, immune-linked module discovery, consensus feature selection, diagnostic modeling, nomogram construction, clinical association, and optional prognostic extension or validation. Covers five study patterns (immune-cell-first diagnostic workflow, immune-module-to-biomarker workflow, consensus-ML biomarker workflow, diagnostic-plus-prognostic hybrid workflow, translational validation workflow) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, reference literature pack, and self-critical risk review.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when performing time-dependent ROC curve analysis for survival data with follow-up time, event status, and a numeric marker. Supports CSV/TXT/TSV/Excel input, `risk_score` as the default marker unless `--marker_col` is provided, parameter validation, standardized output directories, AUC table export, ROC point export, and PDF figure generation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when assessing how well a survival model's predicted probabilities agree with observed outcomes by fitting a Cox model and generating bootstrap calibration curves at one or more prediction horizons from a clinical CSV file. NOT for: nomogram construction, univariate Cox screening, ROC analysis, or decision-curve analysis.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when you need a standardized R CLI workflow to run two-class SVM-RFE feature ranking on an expression-like matrix, choose an informative feature count from cross-validated error, and generate reproducible ranking and error plots. NOT for regression, multi-class classification, missing-value imputation, or remote data fetching.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when building XGBoost models on tabular data and returning feature importance ranking outputs. Supports binary classification and regression with automatic task detection, train-test split, performance tables, feature importance ranking tables, and PNG importance plots.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when evaluating diagnostic biomarker performance from case-control expression data with logistic regression and ROC curves, exporting coefficient and AUC tables together with a ROC PDF. NOT for: survival analysis, time-to-event outcomes, multiclass classification, calibration curves, decision-curve analysis, or nomogram construction.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.