Results for “bio”

Calculates the lowest effective glucocorticoid dose (dexamethasone or prednisone) to normalize blood pressure and serum potassium in glucocorticoid-remediable aldosteronism (GRA/FH-I) by titrating to biochemical and clinical targets. Indicated when initiating medical treatment for confirmed GRA, triggered by findings such as early-onset hypertension, family history of stroke before age 40, spontaneous hypokalemia, or suppressed plasma renin activity with elevated aldosterone.

Query BindingDB for measured protein-ligand binding affinities (Ki, Kd, IC50, EC50) via its keyless REST API or the full TSV download, searching by target (UniProt ID), compound (SMILES), or pathogen. Use when looking up experimental binding constants, profiling inhibitors of a protein target, doing lead optimization, polypharmacology analysis, or structure-activity relationship (SAR) studies; for curated bioactivity mining or drug-like compound library screening at scale prefer alterlab-chembl instead. Part of the AlterLab Academic Skills suite.

Query the STRING API for protein-protein interactions (59M proteins, 20B interactions across 5000+ species), building interaction networks, discovering functional partners, and running GO/KEGG/Pfam enrichment on protein lists. Use when constructing a protein-protein interaction network, expanding from seed proteins to functional partners, or running PPI-based enrichment for systems biology; for curated metabolic pathway maps and reactions prefer alterlab-kegg, and for protein sequences, annotations, or accession ID mapping prefer alterlab-uniprot instead. Part of the AlterLab Academic Skills suite.

Provide direct REST API access to KEGG (academic use only) for pathway analysis, gene-to-pathway and compound-to-pathway mapping, metabolic reactions, KEGG Orthology (KO), drug-drug interactions, and ID conversion. Use when querying KEGG pathways, mapping genes/compounds to metabolic maps, or running KEGG pathway enrichment via raw HTTP/REST; for protein-protein interaction networks prefer alterlab-string-db, for protein sequences and annotations prefer alterlab-uniprot, and for Python workflows spanning many databases prefer bioservices instead. Part of the AlterLab Academic Skills suite.

Creates, analyzes, and visualizes complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing topologies — applicable to social, biological, transportation, citation, and any pairwise-relationship networks. This is classical graph analytics, not deep learning — for training graph neural networks (GCN/message passing, node/edge/graph classification on Cora-style data) use alterlab-torch-geometric instead. Part of the AlterLab Academic Skills suite.

Provide direct REST API access to UniProt (Swiss-Prot/TrEMBL) for protein sequence searches, FASTA retrieval, functional annotations (GO terms, domains), and cross-database ID mapping. Use when looking up a protein entry, fetching a protein FASTA sequence, or mapping accessions between databases over raw HTTP/REST; for EXPERIMENTAL 3D structures prefer alterlab-pdb, for AI-PREDICTED 3D structures prefer alterlab-alphafold-db, for protein-protein interaction networks prefer alterlab-string-db, and for Python workflows spanning many databases prefer bioservices instead. Part of the AlterLab Academic Skills suite.

Access the RCSB Protein Data Bank (PDB) for EXPERIMENTALLY determined 3D structures (X-ray, cryo-EM, NMR) of proteins and nucleic acids — searching by text, sequence, or structure similarity and downloading coordinates in PDB/mmCIF format with metadata. Use when retrieving a structure by PDB ID, running sequence or structure similarity searches, or obtaining experimental coordinates for structural biology and drug discovery; for AI-PREDICTED structures of proteins lacking experimental data prefer alterlab-alphafold-db, and for protein sequences, annotations, or accession ID mapping prefer alterlab-uniprot instead. Part of the AlterLab Academic Skills suite.

GDPR and CCPA/CPRA privacy compliance audit for codebases. Inventories PII fields (email, phone, SSN, IP, device ID, geolocation, biometrics, behavioral data), maps data collection points (forms, APIs, cookies, analytics, error tracking), audits consent mechanisms (cookie banners, opt-in, pre-checked boxes, consent withdrawal), verifies data subject rights implementation (right to access, erasure, rectification, portability, opt-out, Do Not Sell), traces third-party data sharing (Google Analytics, Facebook Pixel, Stripe, SendGrid, Sentry), and checks data retention policies and automated purging. Use when auditing privacy compliance, building data export or deletion endpoints, reviewing cookie consent, or assessing DSAR readiness.

Predict biomolecular complexes with Chai-1, an open AlphaFold3-style model that folds multi-entity assemblies (proteins, ligands, nucleic acids) from a single typed FASTA — strong on antibody–antigen and protein–ligand complexes, with optional MSA and restraint inputs. Use when predicting an antibody–antigen complex, folding a mixed protein/ligand/nucleic-acid assembly described in one FASTA, or generating a complex with experimental restraints. For binding-affinity prediction or a ligand-focused co-fold prefer alterlab-boltz; for protein-only or protein–protein folding prefer alterlab-alphafold; to dock into a fixed receptor prefer alterlab-diffdock. Part of the AlterLab Academic Skills suite.

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.

Extract features from 1D signals using signalTimeFeatureExtractor, signalFrequencyFeatureExtractor, and signalTimeFrequencyFeatureExtractor. Use when computing time-domain features (amplitude, energy, shape factors), frequency-domain features (spectral location, power, bandwidth, PSD), or time-frequency features (spectral shape, instantaneous, ridges, wavelet, EMD-derived) on a per-frame basis. Use when the user asks to "extract features", "compute spectral features", "build a feature table for a classifier", "get per-frame statistics", "run feature extraction on this signal", or describes a vibration / biosignal / radar / sensor signal needing features for downstream ML or analysis. Includes optional GPU acceleration via canUseGPU and gpuArray. Does not cover filter design, audio-specific feature extraction (use audioFeatureExtractor in Audio Toolbox instead), batch dataset orchestration, or 2D / image features.