Deep generative models for single-cell omics; use when you need probabilistic batch correction (scVI), transfer learning, uncertainty-aware differential expression, or multimodal integration (totalVI/MultiVI).
Auto-annotate cell clusters from single-cell RNA data using marker genes.
A Python bioinformatics toolkit for sequence, phylogeny, and microbiome/community-ecology analysis; use it when you need to compute diversity/ordination/statistics from biological data and standard formats (FASTA/FASTQ/Newick/BIOM).
Standard single-cell RNA-seq analysis pipeline. For quality control (QC), normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression analysis, and visualization. Best suited for exploratory single-cell transcriptomics analysis using established workflows. For deep learning models, use scvi-tools; for data format issues, use anndata.
Analyze data with `phylogenetic-tree-styler` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
Use pathology roi selector for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.
A full-featured computational pathology toolkit for advanced WSI analysis, including multiplexed immunofluorescence (CODEX, Vectra), nuclei segmentation, tissue graph construction, and machine learning model training on pathology data. Supports over 160 slide formats. For simple tile extraction from H&E slides, histolab may be simpler.
Scan reagent barcodes or IDs, log expiration dates, and generate multi-level alerts before reagent expiry to support laboratory inventory management.
Compare patient pre-admission medication lists with inpatient orders to automatically identify omitted or duplicated medications and improve medication safety.
Evidence-Based Medicine diagnostic test calculator. Computes sensitivity, specificity, PPV, NPV, likelihood ratios, NNT, and pre/post-test probability from 2x2 contingency table inputs.
A Pythonic wrapper around RDKit with simplified interfaces and sensible defaults. Preferred for standard drug discovery workflows including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformer generation, and parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.
Sort laboratory chemicals into safe storage groups by hazard classification (acids, bases, oxidizers, flammables, toxics). Identifies incompatible pairs, generates storage plans with warnings, and supports OSHA/NFPA compliance for lab safety.
Calculate precise buffer recipes with accurate mass and volume measurements for molecular biology and biochemistry. Supports PBS, RIPA, and TAE with concentration scaling, stock solution preparation, pH adjustment guidance, and step-by-step protocols.
Cloud laboratory platform for automated protein testing and validation; use when you have designed protein sequences and need wet-lab experimental validation (e.g., binding, expression, thermostability, enzyme activity) and API-based submission/status/result retrieval.
Access the ZINC (230M+ purchasable compounds) database when you need to look up compounds by ZINC ID/SMILES, run similarity/analog searches, or download 3D ready-to-dock structures for virtual screening and drug discovery.
Programmatic access to the PubChem database (via PUG-REST API and PubChemPy) for searching chemical compounds, retrieving physicochemical properties, performing structure similarity/substructure searches, and obtaining bioactivity data.
Check if referenced bioinformatics software/code licenses allow commercial use (GPL vs MIT, etc.).
Check for interactions between multiple medications, including severity classification and mechanism explanations.
Diffusion-based molecular docking to predict 3D ligand–protein binding poses (blind docking) with confidence scoring; use when you need pose prediction for drug discovery or virtual screening.
Query the ChEMBL database for bioactive molecules, targets, bioactivities, and approved drugs; use this when you need to filter by physicochemical properties (e.g., MW, LogP), chemical structure (SMILES), or retrieve drug mechanism information.
Automatically identify Western Blot gel bands, perform densitometric analysis, and calculate normalized values relative to loading controls.
