812 件 ・ 関連度順
概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Python library for healthcare ML on EHR data: process MIMIC-III/IV, eICU, OMOP-CDM; encode medical codes (ICD, ATC, NDC); build patient-level datasets; train Transformer, RETAIN, GRASP, MedBERT for mortality, drug recommendation, readmission, diagnosis prediction. Alternatives: FIDDLE (preprocessing), clinical-longformer (clinical NLP), ehr-ml (embeddings).
日本語の概要は準備中です。原文の説明を表示しています。
Per-feature NaN-safe Spearman/Pearson correlation across many features (genes, proteins, variants) with missing values. Covers why bulk matrix shortcuts fail, correct pairwise deletion, degenerate input filtering, and large-dataset performance. Use statistical-analysis for test choice; shap-model-explainability for interpretability.
日本語の概要は準備中です。原文の説明を表示しています。
Structured hypothesis formulation: turn observations into testable hypotheses with predictions, propose mechanisms, design experiments. Follows the scientific method. Use scientific-brainstorming for open ideation; hypogenic for automated LLM hypothesis testing on datasets.
日本語の概要は準備中です。原文の説明を表示しています。
Parse/write FCS (Flow Cytometry) files v2.0-3.1. Events as NumPy, channel metadata, multi-dataset files, CSV/FCS export. Use FlowKit for gating/compensation.
日本語の概要は準備中です。原文の説明を表示しています。
Query and download NCI Imaging Data Commons (IDC) cancer radiology and pathology datasets via the idc-index Python client. No authentication required: the parquet index ships inside the pip wheel, SQL runs locally via DuckDB, and DICOM downloads stream from public S3/GCS buckets through s5cmd. Use sql_query() for DuckDB cohort selection, get_collections/get_patients/get_dicom_studies/get_dicom_series for hierarchical browsing, download_from_selection() for downloads, and get_viewer_URL() for OHIF/Slim links. Use pydicom-medical-imaging for local DICOM reading; histolab for whole-slide pathology preprocessing.
日本語の概要は準備中です。原文の説明を表示しています。
WSI processing for digital pathology. Tissue detection, tile extraction (random, grid, score-based), filter pipelines for H&E/IHC. For dataset prep, tile-based DL, slide QC. Use pathml for multiplexed imaging.
日本語の概要は準備中です。原文の説明を表示しています。
Three-tiered sgRNA design guide using validated Addgene sequences, CRISPick pre-computed datasets, or de novo design rules for CRISPR experiments
日本語の概要は準備中です。原文の説明を表示しています。
Search the PRIDE Archive v3 REST API for proteomics datasets: discover projects by keyword + faceted filters (organism, instrument, disease, software), fetch project metadata, list and download RAW/PEAK/RESULT/FASTA files (with FTP/Aspera URLs), look up which projects mention a UniProt accession, and find similar projects. PRIDE v3 no longer exposes peptide/PSM-level identification endpoints — for spectrum-level data download the project's RESULT files. Use uniprot-protein-database for protein sequences; interpro-database for domain architecture.
日本語の概要は準備中です。原文の説明を表示しています。
Compile NYC landmarks, DOB, ACRIS, HPD, and BSA findings into one property due-diligence report. Use for a full report; use individual NYC skills for one dataset.
日本語の概要は準備中です。原文の説明を表示しています。
Read, decompose, and analyze IEEE-style papers using routed technical-reading workflows. Use when extracting problem setting, object-method-condition logic, contributions, method pipeline, equations, datasets, baselines, metrics, ablations, limitations, replication details, citation role, related-work positioning, implementation notes, or reviewer-style assessment from a paper, PDF, abstract, or manuscript excerpt.
日本語の概要は準備中です。原文の説明を表示しています。
You MUST use this before any data analysis or investigation - before exploring a dataset, loading or profiling data, running a model, computing a statistic, or testing an idea, and before any outcome data is touched
日本語の概要は準備中です。原文の説明を表示しています。
Use when facing 2+ independent investigations that can proceed without shared state - parallel literature survey, multi-dataset replication, or pre-specified robustness checks
日本語の概要は準備中です。原文の説明を表示しています。
Analyze large-scale construction datasets. Process thousands of projects for patterns, benchmarks, and predictive insights.
日本語の概要は準備中です。原文の説明を表示しています。
Integrate open construction datasets. Combine open data sources for enhanced analysis
日本語の概要は準備中です。原文の説明を表示しています。
Comprehensive Pandas toolkit for construction data analysis. Filter, group, aggregate BIM elements, calculate quantities, merge datasets, and generate reports from structured construction data.
日本語の概要は準備中です。原文の説明を表示しています。
MLflow 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up trace ingestion and production monitoring, aligning judges with MemAlign from domain expert feedback, or running optimize_prompts() with GEPA for automated prompt improvement.
日本語の概要は準備中です。原文の説明を表示しています。
Build RAG / unstructured-document evaluation datasets and demo documents (e.g. for Knowledge Assistant) on Databricks: generate synthetic PDFs locally, upload to Unity Catalog volumes, and pair each document with test questions for retrieval evaluation.
日本語の概要は準備中です。原文の説明を表示しています。
Drive the agentkit CLI (aliases agentkit / ak) to scaffold, build, deploy, and manage agents on Volcengine AgentKit from the terminal — runtimes, knowledge bases, memory collections, tools, harnesses, invocation, SSO auth, sandboxes, and evaluation datasets. Use when a user wants to create/deploy an agent project, call or inspect a deployed runtime, manage AgentKit resources, or resolve a CLI/auth/deploy error. Does NOT handle non-AgentKit deployment, general Docker/k8s operations, Volcengine IAM management, or writing agent application code (use veadk-agent-development for that).
日本語の概要は準備中です。原文の説明を表示しています。
Routes multi-tool workflows through MCP servers for large datasets and pipelines. Use when Bash tool overhead is limiting throughput on data-heavy tasks.
日本語の概要は準備中です。原文の説明を表示しています。
Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.
日本語の概要は準備中です。原文の説明を表示しています。
Pick which precise covariance estimator to use for a given dataset. Use when you have data X and are unsure which estimator fits its dimension, conditioning, or tail behavior. Wraps precise.suggest() and covariance_features().
日本語の概要は準備中です。原文の説明を表示しています。