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Constraint-based metabolic modeling (COBRA) covers FBA, FVA, gene knockouts, flux sampling, and SBML models for systems biology and metabolic engineering analysis.

Deep generative models for single-cell omics are ideal for advanced modeling, batch effects, and multimodal data, offering probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, and multi-modal integration (TOTALVI, MultiVI). For standard analysis pipelines, use scanpy.

Build and analyze phylogenetic trees using MAFFT, IQ-TREE 2, and FastTree. Visualize them with ETE3 or FigTree for applications in evolutionary analysis, microbial genomics, and molecular clock studies.

Perform zero-shot time series forecasting with Google's TimesFM foundation model. It handles any univariate time series (sales, sensors, energy, vitals, weather) without custom model training, supporting CSV/DataFrame/array inputs for point forecasts and prediction intervals, and includes a preflight system checker.

A hardware-agnostic quantum ML framework with automatic differentiation, ideal for training quantum circuits via gradients, building hybrid quantum-classical models, and ensuring device portability across major quantum platforms. It's best for variational algorithms, quantum neural networks, and integration with PyTorch/JAX/TensorFlow.

Query the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biological data including genes, drugs, diseases, phenotypes, and more.

A computational materials science toolkit for crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, and format conversion.

A Python framework for computational fluid dynamics simulations, supporting Navier-Stokes (2D/3D), shallow water, and stratified flows, as well as turbulence and vortex dynamics analysis. It offers pseudospectral methods with FFT, HPC support, and comprehensive output analysis.

PyHealth enables building deep-learning pipelines for healthcare, supporting the loading of diverse EHR/signal/imaging datasets, defining tasks like mortality prediction or drug recommendation, and training with various models.

Utilizes PyTorch Lightning to organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, and implement data pipelines, callbacks, logging, and distributed training for scalable neural network training.

Standard single-cell RNA-seq analysis pipeline for quality control, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Ideal for exploratory scRNA-seq analysis using established workflows.

Search 78 public scientific, biomedical, materials science, and economic databases via REST APIs, covering fields like physics, chemistry, biology, and materials.