Results for “bioblend”

Provides reference documentation and code patterns for using Biopython to handle biological sequences, file formats, database access, alignments, structures, and phylogenetics.

Provides Python tools for biological computation, including sequence manipulation, file I/O, database access, structural bioinformatics, and phylogenetics.

Processes and analyzes multiple physiological signals (ECG, respiration, EDA, EMG, PPG, EOG) together using NeuroKit2, including cross-signal features like RSA and event-related analysis.

Use when working from the local Biomni repository to run agent-style biomedical tasks or inspect Biomni's biomedical tool modules and examples.

Run computational biology tools for protein structure prediction, design, docking, and molecular dynamics on managed cloud GPUs via REST API or MCP server.

Translates a Chinese article into English Markdown, generates bilingual detailed summaries, and writes the results back to LanceDB while preserving structure.

Framework autônomo de agente de IA biomédica para executar tarefas de pesquisa complexas em genômica, descoberta de fármacos, biologia molecular e análise clínica. Use esta skill ao conduzir pesquisa biomédica em múltiplas etapas, incluindo design de triagem CRISPR, análise de RNA-seq de células únicas, previsão ADMET, interpretação GWAS, diagnóstico de doenças raras ou otimização de protocolos de laboratório. Aproveita o raciocínio de LLM com execução de código e bancos de dados biomédicos integrados.

Co-fold biomolecular complexes with Boltz-2, an open AlphaFold3-style model — predict protein + ligand (SMILES/CCD), protein + nucleic-acid, and multi-chain structures in one pass, with binding-affinity prediction. Use when folding a protein together with a small-molecule ligand, predicting a holo (ligand-bound) complex or its binding affinity, or co-folding protein–DNA/RNA assemblies. For protein-only or protein–protein folding without ligands prefer alterlab-alphafold; for antibody–antigen complexes prefer alterlab-chai; to dock a ligand into a FIXED receptor structure prefer alterlab-diffdock; to look up an existing structure prefer alterlab-pdb. Part of the AlterLab Academic Skills suite.

Build clinical deep-learning pipelines with PyHealth: load EHR, signal, and imaging datasets, define prediction tasks, instantiate models, train with the PyHealth Trainer, and compute clinical metrics.