3. Data, Science & AI

Programmatically queries the CELLxGENE Census, a comprehensive single-cell gene expression atlas with 61M+ cells for research across tissues, diseases, and cell types.

A low-level Python library for generating customizable static visualizations in publication formats like PNG, PDF, and SVG.

Accesses and retrieves gene expression data from NCBI GEO, including microarray and RNA-seq datasets, for transcriptomics and expression analysis.

Provides structured hypothesis formulation from experimental data, following the scientific method to generate testable predictions and experimental designs.

Provides production-ready reinforcement learning algorithms (PPO, SAC, DQN) with scikit-learn-like API for standard RL experiments and prototyping.

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

Creates publication-ready scientific figures with multi-panel layouts, error bars, and journal-specific formatting using matplotlib, seaborn, and plotly.

Infers gene regulatory networks from transcriptomics data using GRNBoost2 and GENIE3 for TF-target gene relationships.

Enables distributed computing for pandas and NumPy workflows, scaling beyond memory limits and across clusters for parallel processing and distributed machine learning.

End-to-end analysis of Neuropixels neural recordings with spike sorting, motion correction, and AI-assisted curation for electrophysiology research.

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

Fast CLI/Python tool for interactive queries across 20+ bioinformatics databases including gene info, BLAST, and AlphaFold structures.

Automates exploratory data analysis for scientific datasets, detecting file types and generating markdown reports with quality metrics and recommendations.

Provides specialized machine learning tools for time series analysis including forecasting, anomaly detection, and clustering with scikit-learn compatible APIs.

Enables efficient storage and parallel access of large scientific datasets using chunked arrays with cloud storage integration and scientific library compatibility.

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

Provides SHAP-based model interpretability and explainability for machine learning, including feature importance and plot generation (waterfall, beeswarm, etc.).

Simplified Python interface for RDKit, enabling efficient drug discovery tasks including SMILES parsing and molecular standardization.

Comprehensive toolkit for developing, testing, and deploying machine learning models with clinical data and healthcare datasets.

Framework for developing and running quantum circuits on IBM Quantum hardware, featuring error mitigation and enterprise deployment tools.

Processes and analyzes massive tabular datasets (billions of rows) using out-of-core operations, lazy evaluation, and efficient visualization without exceeding memory limits.

Accesses and analyzes comprehensive drug data from DrugBank, including properties, interactions, targets, and pharmacology for research and discovery.

Predicts protein-ligand binding poses using diffusion models from PDB/SMILES data, providing confidence scores for virtual screening in drug design.

Queries Reactome REST API for pathway analysis, gene mapping, and systems biology research in biological data studies.