3. Data, Science & AI

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

Provides efficient similarity search and clustering for dense vectors, supporting billions of vectors with GPU acceleration and multiple index types for high-performance AI applications.

Processes mass spectrometry data (mzML/MGF/MSP) with spectral similarity calculations and metadata harmonization for metabolomics research.

Comprehensive Python toolkit for survival analysis, including Cox models, Random Survival Forests, and evaluation metrics for time-to-event data.

Provides advanced computer vision capabilities including object detection, segmentation, and model deployment using PyTorch, OpenCV, and vision transformers for production AI systems.

Offers AI-ready drug discovery datasets including ADME, toxicity, and DTI with benchmarks and scaffold splits for therapeutic machine learning.

Queries Reactome REST API for pathway analysis, gene-pathway mapping, and molecular interaction studies in systems biology research.

Expert guidance for fast fine-tuning of AI models using Unsloth, achieving 2-5x speedup and 50-80% memory reduction via LoRA/QLoRA optimization.

Scalable data processing for ML workloads, supporting Parquet, CSV, JSON, and images. Integrates with Ray Train, PyTorch, TensorFlow for distributed ETL and preprocessing.

Provides molecular data processing including SMILES parsing, descriptor calculation, and substructure search for cheminformatics applications.

A Python library for efficient storage and processing of large scientific datasets using chunked arrays, with cloud storage integration and compatibility with NumPy, Dask, and Xarray.

Provides a state-space model architecture (Mamba) for efficient sequence processing with O(n) complexity, 5× faster inference, and million-token sequence support without KV cache.

Enables genomics pipeline development and execution on DNAnexus cloud platform via dxpy SDK for data management and analysis of FASTQ/BAM/VCF formats.

Interactive Python library for creating scientific, statistical, and financial visualizations including charts, plots, and dashboards with customizable options.

Detects and reports system resources (CPU, GPU, memory) to guide computational strategy for scientific tasks, including recommendations for parallel processing and GPU acceleration.

Guides mechanistic interpretability research using TransformerLens to inspect transformer internals, attention patterns, and activation patching experiments.

Provides quantum simulation and analysis for quantum systems, including states, operators, and dynamics, using QuTiP.

Query ChEMBL database for bioactive molecules, retrieve bioactivity data (IC50, Ki), and perform structure-activity relationship studies in medicinal chemistry.

Provides type-safe structured output generation for AI models using Pydantic, ensuring valid JSON/XML/code with local model support and optimized speed.

Comprehensive single-cell RNA-seq analysis pipeline including data loading, QC, dimensionality reduction, clustering, and cell type annotation using Scanpy.

Statistical modeling toolkit providing OLS, GLM, logistic regression, ARIMA, and hypothesis tests for data analysis and econometric research.

DeepChem enables molecular property prediction, GNN-based drug discovery modeling, and benchmarking via MoleculeNet for pharmaceutical applications.

Conducts differential gene expression analysis on RNA-seq data with DESeq2, including statistical testing and visualization.

Provides tools for creating, editing, and analyzing spreadsheet data with formulas, formatting, and visualization for .xlsx and .csv files.