Skill UI

Integrate with the DNAnexus cloud genomics platform to build apps/applets, manage FASTQ/BAM/VCF data, run workflows, and script automations via dxpy for biomedical pipelines.

Conduct rigorous, multi-database literature reviews with systematic workflows, verified citations in styles like APA or Nature, and AI-enhanced schematics for publication-ready markdown and PDF outputs in biomedical and technical domains.

TorchDrug is a PyTorch-native toolbox for building graph neural networks tailored to molecules, proteins, and biomedical knowledge graphs, covering property prediction, generation, retrosynthesis, and knowledge graph reasoning with curated datasets and models.

Collection of battle-tested PyTorch training recipes covering architecture selection, optimizers, LR scheduling, precision tips, and debugging to train or fine-tune LLMs, vision, diffusion, and biomedical models with high GPU throughput.

This guide provides a comprehensive workflow for searching and retrieving biomedical literature from PubMed and NCBI. It covers advanced query construction using MeSH terms and specific field tags, advanced filtering (date, publication type), and a repeatable programmatic workflow using NCBI E-utilities (esearch, efetch). Essential for researchers conducting systematic reviews and literature monitoring.

The BIDS skill provides a comprehensive framework for organizing, standardizing, and managing complex neuroscience and biomedical datasets. It covers querying, validating, and converting various modalities (MRI, EEG, MEG, etc.) to ensure compliance with the BIDS standard for research reproducibility and efficient data sharing.

A powerful tool for deterministic and reproducible retrieval of evidence from specialized academic and policy corpora. It supports searching arXiv (preprints), PubMed/PMC (biomedical literature), and US policy documents. Users can check corpus freshness, retrieve stable metadata, and fetch full-document Markdown for high-reliability research tasks.

This guide provides a comprehensive workflow for searching and retrieving biomedical literature from PubMed and NCBI. It covers advanced query construction using MeSH terms and specific field tags, advanced filtering (date, publication type), and a repeatable programmatic workflow using NCBI E-utilities (esearch, efetch). Essential for researchers conducting systematic reviews and literature monitoring.

Provides a comprehensive, step-by-step workflow for conducting rigorous literature reviews across academic, biomedical, and technical domains. It guides users through defining research questions (PICO), developing systematic search protocols, evidence logging, source screening, structured data extraction, and synthesizing findings to build high-quality, reproducible review reports.

This skill programmatically searches and fetches biomedical literature from PubMed and PMC using NCBI E-utilities (ESearch, EFetch, ESummary). It allows users to gather large corpora of abstracts, full texts, and metadata related to specific conditions, drugs, or genes. Ideal for building evidence bases, performing biomedical Named Entity Recognition (NER), or summarizing scientific findings.

This skill interfaces with the modern v2 REST API of ClinicalTrials.gov, allowing users to search for clinical studies matching specific conditions, interventions, and recruitment statuses. It is essential for building patient-to-trial matching features, generating a corpus of trial records for biomedical NER, or finding open studies for a given diagnosis or drug. It uses cursor pagination and requires no API key.