데이터 분석, 머신러닝, LLM 앱, 프롬프트 설계 (9.1천건)
Check whether a NYC building is landmarked or in a historic district using LPC data. Use when the user asks "is this building landmarked", whether a site sits in a historic district, or whether LPC review applies. NYC only.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Parse a SIF (Standard Interchange Format) input into a readable preview and optionally append canonical product rows to the project's CSV library. Use when asked to convert or inspect SIF dealer data. For the reverse direction use /as:csv-to-sif.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Extract structured FF&E specs from a list of product URLs into a schedule. Use to pull or bulk-import product-page data; not for PDF catalogs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Visium and other spot-based spatial transcriptomics (Slide-seq, ST) cell type deconvolution with cell2location: load Space Ranger output, get a tissue-matched scRNA-seq reference from CELLxGENE, learn cell type signatures, estimate cell type abundance per spot with posterior uncertainty, and check the maps against marker genes. Default route for 'annotate cell types' on Visium when a GPU is available. Limits: needs a tissue- and state-matched reference; the default 30000 epochs take hours on CPU, so without a GPU it runs only with fewer epochs (exploratory maps) and can exceed one execution call's time limit. For fast per-spot proportions on CPU use spatial-mapping-tangram (clusters mode); for single-cell-resolution data (Xenium, MERFISH, CosMx) use spatial-transcriptomics-cell-type-annotation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Spatial domain detection (tissue regions / niches) for spatial transcriptomics: SpaGCN (expression + spatial graph + H&E histology, Visium/ST), GraphST (self-supervised graph contrastive embedding, spots or cells) and UTAG (spatial message passing for Xenium/MERFISH/CosMx/IMC cells). Outputs a domain label per spot or cell, boundary refinement, cross-method agreement (ARI) and domain marker genes. For niches defined by the cell type composition of annotated cells use Step 7 of spatial-transcriptomics-cell-type-annotation; for neighbourhood enrichment and spatial statistics use spatial-transcriptomics-analysis-squidpy.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Interactive visualization with Plotly. 40+ chart types (scatter, line, heatmap, 3D, geographic) with hover, zoom, pan. Two APIs: Plotly Express (DataFrame) and Graph Objects (fine control). For static publication figures use matplotlib; for statistical grammar use seaborn.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Statistical visualization on matplotlib + pandas. Distributions (histplot, kdeplot, violin, box), relational (scatter, line), categorical, regression, correlation heatmaps. Auto aggregation/CIs. Use plotly for interactive; matplotlib for low-level.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Query CELLxGENE Census (61M+ cells). Search by cell type/tissue/disease/organism; get AnnData, stream out-of-core, train PyTorch models. For your own data use scanpy; for annotated data use anndata.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Counts RNA-seq reads overlapping GTF gene features. Takes sorted STAR BAMs plus GTF; outputs a per-gene tab-delimited matrix across samples. Handles strandedness (0/1/2), paired-end, multi-sample batch counting in one command, and outputs assignment statistics. Use Salmon for alignment-free quantification; use featureCounts when STAR BAMs already exist.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Python library for genomic interval ML. Train/apply region2vec embeddings turning BED regions into vectors, index interval datasets for ML, search embedding space with BEDSpace, and evaluate embedding quality. Use for chromatin accessibility clustering, regulatory element classification, and cross-sample region comparison.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Annotated matrices for single-cell genomics. Stores X with obs/var metadata, layers, embeddings (obsm/varm), graphs (obsp/varp), uns. Use for .h5ad/.zarr I/O, concatenation, scverse integration. For analysis use scanpy; for probabilistic models use scvi-tools.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Rust-backed Python library for fast genomic token arithmetic and BED processing. High-performance BED I/O, interval set ops (intersect, merge, complement, subtract), region tokenization against a universe, universe construction. Use for preprocessing large BED collections and ML token vocabularies.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Harmony batch correction for scRNA-seq and other omics. Removes batch effects from PCA embeddings while preserving biology. Run after PCA, before UMAP. Scales to millions of cells. Python (harmonypy, scanpy) and R (Seurat).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Three-tiered approach to omics data analysis (transcriptomics, proteomics) covering validated pipelines, standard workflows, and custom methods
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Multi-modal single-cell analysis with muon/MuData. Joint RNA+ATAC (10x Multiome), CITE-seq (RNA+protein), other multi-omics. MuData holds per-modality AnnData with shared obs. WNN joint embedding, per-modality preprocessing, MOFA factor analysis. Use scanpy-scrna-seq for single-modality RNA; use muon when combining 2+ omics from the same cells.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Therapeutics Data Commons (TDC) AI-ready drug discovery datasets. Curated ADME, toxicity, DTI, DDI with scaffold/cold splits, standardized metrics, molecular oracles, and ADMET benchmarks for therapeutic ML and property prediction. For chemical database queries use chembl-database-bioactivity; for featurization use molfeat.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Molecular featurization hub (100+ featurizers) for ML. SMILES to fingerprints (ECFP, MACCS, MAP4), descriptors (RDKit 2D, Mordred), pretrained embeddings (ChemBERTa, GIN, Graphormer), pharmacophores. Scikit-learn compatible with parallelization/caching. For QSAR, virtual screening, similarity, and molecular DL.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Designing scientific schematics, diagrams, and graphical abstracts. Covers tool selection (BioRender, Inkscape, Affinity, PowerPoint), design principles for pathway diagrams, mechanism schematics, experimental workflows, and journal graphical abstracts. Includes composition, icon sourcing, color for biological entities, and accessibility. Use when creating illustrative (not data-driven) scientific figures.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
PyTorch-based ML platform for drug discovery: graph molecular representation learning, property prediction (ADMET, activity), retrosynthesis, drug-target interaction (DTI), and pretraining on large molecular datasets. Provides GNN layers (GraphConv, GAT, MPNN), pretrained models, and benchmark datasets.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Core Python library for astronomy/astrophysics: units with dimensional analysis, celestial coordinate transforms (ICRS/Galactic/AltAz/FK5), FITS I/O, tables (FITS/HDF5/VOTable/CSV), cosmology (Planck18, distance/age), precise time (UTC/TAI/TT/TDB, Julian, barycentric), WCS pixel-world mapping, model fitting. For general tables use pandas/polars; for radio interferometry use CASA.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
MATLAB/GNU Octave numerical computing: matrices, linear algebra, ODEs, signal processing, optimization, statistics, scientific visualization. MATLAB-syntax examples run on both. For Python use numpy/scipy; for statistical modeling use statsmodels.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Geospatial vector analysis extending pandas. Read/write spatial formats (Shapefile, GeoJSON, GeoPackage, Parquet, PostGIS), CRS handling, geometric ops (buffer, simplify, centroid, affine), spatial analysis (joins, overlays, dissolve, clipping, distance), visualization (choropleth, interactive maps, basemaps). Use for spatial joins, overlays, CRS transforms, area/distance, maps.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.