3. Data, Science & AI

High-performance reinforcement learning framework for fast parallel training, vectorized environments, and multi-agent systems with game environment integrations.

Enables Bayesian modeling with PyMC, including hierarchical models, MCMC (NUTS), variational inference, and model comparison (LOO/WAIC).

Processes FCS flow cytometry data files, extracting events and metadata into NumPy arrays and DataFrames for scientific analysis and preprocessing.

Provides scikit-learn compatible tools for time series classification, regression, forecasting, anomaly detection, and segmentation analysis.

Comprehensive Python library for astronomical data analysis, including coordinate transformations, FITS file handling, and cosmological calculations.

Cloud platform automating protein testing, validation, and AI-driven sequence optimization using tools like ESM and SoluProt for experimental workflows.

High-performance Rust toolkit with Python bindings for genomic interval analysis, BED file processing, and ML-ready tokenization.

Predicts protein-ligand binding poses using diffusion-based AI, supporting PDB and SMILES inputs for structure-based drug design.

Queries NCBI Gene database via E-utilities/Datasets API to retrieve gene information including RefSeqs, GO terms, locations, and phenotypes for annotation and functional analysis.

Analyzes Neuropixels neural recordings including data loading, spike sorting, quality metrics, and AI-assisted curation for extracellular electrophysiology research.

Queries Open Targets Platform for target-disease associations, omics evidence, and drug target data to identify therapeutic targets in biomedical research.

Provides production-ready reinforcement learning algorithms (PPO, SAC, DQN, etc.) with a scikit-learn-like API for single-agent RL experiments in Gymnasium environments.

Infers gene regulatory networks from transcriptomics data using ML algorithms (GRNBoost2, GENIE3) for identifying transcription factor-target gene relationships.

Accesses ClinPGx pharmacogenomics database for querying gene-drug interactions, CPIC guidelines, and allele functions to support precision medicine and dosing decisions.

Provides direct REST API access to PubMed for advanced Boolean/MeSH queries, batch processing, and citation management in scientific workflows.

Programmatically accesses NIH Metabolomics Workbench database to query metabolites, MS/NMR data, and study metadata for biomarker discovery research.

Fast CLI/Python tool for interactive bioinformatics database queries including gene info, BLAST, and AlphaFold structures.

Simplifies RDKit usage for drug discovery tasks including SMILES parsing, molecular descriptors, and 3D conformer generation.

Enables genomics data management, pipeline execution, and workflow automation via DNAnexus cloud platform and dxpy SDK for bioinformatics applications.

DeepTools processes and visualizes NGS data including ChIP-seq, RNA-seq, and ATAC-seq analysis with features like BAM to bigWig conversion and QC metrics.

Provides a Python-based platform for managing microscopy data, including image access, pixel analysis, ROI management, and batch processing for high-content screening workflows.

Provides a unified Python interface for querying 40+ bioinformatics databases (e.g., UniProt, KEGG) in single workflows, enabling cross-database analysis and ID mapping.

Provides a data structure for annotated matrices in single-cell analysis, supporting .h5ad file format and scverse ecosystem integration.

Advanced toolkit for computational pathology: WSI analysis, nucleus segmentation, tissue graph construction, and ML model training on pathology data.