Skill UI

PyDESeq2 is a robust Python implementation for conducting differential gene expression analysis on bulk RNA-seq count data. It supports complete statistical workflows, including data loading from various formats, complex multi-factor design specifications (e.g., accounting for batch effects or covariates), normalization, and performing advanced Wald tests. It is ideal for researchers transitioning R-based DESeq2 analyses into scalable Python pipelines.

A comprehensive Python library for handling DICOM (Digital Imaging and Communications in Medicine) files. Use this skill for reading, writing, and manipulating complex medical images such as CT, MRI, and X-ray. Features include metadata extraction, pixel data manipulation, image anonymization (PHI removal), multi-format conversion, and processing multi-slice volumes, making it essential for medical data analysis and PACS integration.

PyHealth is a comprehensive Python toolkit designed for building deep-learning pipelines in the clinical and healthcare domains. It supports diverse data modalities, including EHR records, physiological signals, and medical images. The framework follows a stable 5-stage pipeline (Dataset → Task → Model → Trainer → Metrics), enabling users to perform complex tasks like predicting mortality, optimizing drug recommendations, staging sleep, or cross-mapping medical codes (ICD/ATC/RxNorm) on datasets like MIMIC-III/IV and OMOP.

PyLabRobot is a vendor-agnostic, pure Python SDK designed for controlling complex, autonomous laboratory workflows. It provides a unified interface to manage diverse equipment—including liquid handlers (Hamilton, Opentrons, Tecan), plate readers, pumps, and shakers—enabling users to simulate, develop, and execute reproducible, multi-device protocols across different operating systems.

Pymatgen is a comprehensive Python toolkit designed for computational materials science. It enables users to create, analyze, and manipulate complex crystal structures, calculate electronic properties (DOS, band structures), simulate phase transitions, and interact with large material databases like the Materials Project. Ideal for solid-state physics, chemistry, and advanced materials research.

PyMC is a comprehensive Python library for probabilistic programming and Bayesian modeling. It enables users to build, fit, and validate complex statistical models, including hierarchical structures, time series, and regression models. It provides powerful tools for MCMC sampling (NUTS) and variational inference, making it essential for rigorous uncertainty quantification and model comparison (LOO/WAIC).

Pymoo is a comprehensive Python framework designed for solving both single-objective and multi-objective optimization problems. It implements state-of-the-art evolutionary algorithms (like NSGA-II, NSGA-III, MOEA/D) and is specialized in identifying Pareto-optimal fronts. This tool is essential for engineering design and scientific problems requiring the analysis of trade-offs among multiple conflicting objectives.

A comprehensive Python wrapper for OpenMS, designed for sophisticated computational mass spectrometry workflows. It supports end-to-end analysis in proteomics and metabolomics, enabling feature detection, peptide/protein identification, quantitative analysis (label-free/isobaric), and adduct/accurate mass annotation. Ideal for researchers processing complex LC-MS/MS raw data and running high-level pipelines.

Pysam is a comprehensive Python toolkit for handling genomic data. It provides a Pythonic interface for reading, writing, and manipulating essential genomic formats, including SAM/BAM/CRAM alignments, VCF/BCF variants, and FASTA/FASTQ sequences. It is critical for developing robust bioinformatics pipelines, performing coverage analysis, and analyzing genetic variations.

PyTDC (Therapeutics Data Commons) is an open-science platform providing comprehensive, AI-ready datasets and benchmarks for the entire drug discovery and development pipeline. It supports single-instance prediction (e.g., ADME, toxicity), multi-instance prediction (DTI, DDI, PPI), and novel molecule generation (MolGen). Use this skill for benchmarking machine learning models and predicting complex biomedical properties.

A comprehensive framework designed to structure and automate PyTorch deep learning workflows. It simplifies boilerplate code by organizing models into LightningModules, automating training loops via the Trainer, and providing robust support for multi-GPU/TPU scaling (DDP, FSDP, DeepSpeed), data handling (DataModules), and best-in-class experiment logging (W&B, MLflow). Ideal for professional, scalable deep learning project development.

Pyzotero is a powerful Python wrapper for the Zotero Web API v3. It enables developers to programmatically interact with Zotero libraries, allowing for CRUD operations on items, collections, tags, and attachments. Use this skill to build robust research automation workflows, manage bibliographic references, export citations (e.g., BibTeX), or perform bulk data processing within a Zotero environment.