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Add support for PyDicom

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Timothy Kassis
2025-10-23 09:35:46 -07:00
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- **matchms** - Processing and similarity matching of mass spectrometry data with 40+ filters, spectral library matching (Cosine, Modified Cosine, Neutral Losses), metadata harmonization, molecular fingerprint comparison, and support for multiple file formats (MGF, MSP, mzML, JSON)
- **pyOpenMS** - Comprehensive mass spectrometry data analysis for proteomics and metabolomics (LC-MS/MS processing, peptide identification, feature detection, quantification, chemical calculations, and integration with search engines like Comet, Mascot, MSGF+)
## Medical Imaging
- **pydicom** - Pure Python package for working with DICOM (Digital Imaging and Communications in Medicine) files. Provides comprehensive support for reading, writing, and manipulating medical imaging data from CT, MRI, X-ray, ultrasound, PET scans and other modalities. Key features include: pixel data extraction and manipulation with automatic decompression (JPEG/JPEG 2000/RLE), metadata access and modification with 1000+ standardized DICOM tags, image format conversion (PNG/JPEG/TIFF), anonymization tools for removing Protected Health Information (PHI), windowing and display transformations (VOI LUT application), multi-frame and 3D volume processing, DICOM sequence handling, and support for multiple transfer syntaxes. Use cases: medical image analysis, PACS system integration, radiology workflows, research data processing, DICOM anonymization, format conversion, image preprocessing for machine learning, multi-slice volume reconstruction, and clinical imaging pipelines
## Protein Engineering & Design
- **ESM (Evolutionary Scale Modeling)** - State-of-the-art protein language models from EvolutionaryScale for protein design, structure prediction, and representation learning. Includes ESM3 (1.4B-98B parameter multimodal generative models for simultaneous reasoning across sequence, structure, and function with chain-of-thought generation, inverse folding, and function-conditioned design) and ESM C (300M-6B parameter efficient embedding models 3x faster than ESM2 for similarity analysis, classification, and feature extraction). Supports local inference with open weights and cloud-based Forge API for scalable batch processing. Use cases: novel protein design, structure prediction from sequence, sequence design from structure, protein embeddings, function annotation, variant generation, and directed evolution workflows