3. Data, Science & AI

Multi-objective optimization framework with NSGA-II, NSGA-III, and Pareto front handling for engineering design and scientific problem-solving.

Integrates with DNAnexus for genomics data management, pipeline execution, and dxpy SDK-based workflow automation.

Provides molecular featurization for machine learning, including 100+ featurizers, SMILES to features conversion, and support for QSAR and molecular ML applications.

Performs symbolic mathematics in Python, including algebra, calculus, and equation solving for exact results.

Performs constraint-based metabolic modeling including flux balance analysis, gene knockouts, and flux sampling for systems biology research and metabolic engineering applications.

Toolkit for protein language models (ESM3, ESM C) enabling sequence, structure, and function prediction, protein design, and engineering via local/cloud inference.

Cloud platform automating protein testing, validation, and AI-driven sequence optimization using computational tools like ESM.

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

Accesses European Nucleotide Archive (ENA) via API/FTP to retrieve DNA/RNA sequences, FASTQ files, and genome assemblies for bioinformatics workflows.

Standard pipeline for single-cell RNA-seq analysis including QC, normalization, dimensionality reduction, clustering, and visualization.

DeepTools provides genomic data analysis for NGS workflows, including BAM to bigWig conversion, QC metrics, and visualization for ChIP-seq, RNA-seq, and ATAC-seq.

Provides molecular machine learning tools with diverse featurizers and pre-built datasets for property prediction using traditional ML or GNNs.

Statistical visualization library for exploratory data analysis, built on Matplotlib and integrated with pandas for distributions and relationships.

Cloud-based platform for quantum chemistry computations, offering molecular property prediction, protein-ligand docking, and AI-driven molecular simulations via Python API.

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.