Skill UI

Hypogenic is a powerful framework designed to accelerate scientific discovery by automating the process of hypothesis generation and testing on complex tabular datasets. It supports three advanced modes: HypoGeniC for purely data-driven pattern discovery; HypoRefine for synergistically integrating literature insights with empirical data; and Union methods for comprehensive hypothesis coverage. Ideal for domains like deception detection, content analysis, and advanced predictive modeling where systematic exploration of multiple theories is required.

Latch is a powerful Python framework designed for building and deploying scalable, serverless bioinformatics pipelines. It allows users to define complex workflows using Python decorators, seamlessly integrate standard tools like Nextflow and Snakemake, and manage cloud data through LatchFile/LatchDir abstractions. Featuring advanced resource configuration, GPU support, and pre-verified pipelines for tasks such as RNA-seq and AlphaFold, it ensures high reproducibility and efficiency in computational biology.

Matchms is a powerful open-source Python library designed for comprehensive mass spectrometry data processing and analytical workflows. It enables users to import spectra from various formats (mzML, MGF, etc.), apply advanced filtering (metadata harmonization, peak selection), and calculate various spectral similarities (e.g., Cosine, Modified Cosine). It is invaluable for metabolite identification, library searching, and building reproducible metabolomics pipelines.

Molfeat is a comprehensive Python library designed to convert chemical structures, such as SMILES strings, into numerical feature vectors. It unifies over 100 pre-trained and hand-crafted molecular featurizers (e.g., ECFP, MACCS, ChemBERTa). This tool is essential for building Quantitative Structure-Activity Relationship (QSAR) models, conducting virtual screening, performing similarity searches, and training deep learning models on molecular datasets.

NetworkX is a powerful Python library used for generating, manipulating, and analyzing complex networks and graphs. It provides comprehensive tools for running essential graph algorithms (e.g., shortest paths, centrality, community detection), modeling diverse structures (social, biological, transportation), and handling various data formats (GraphML, JSON, Pandas). Ideal for system modeling and scientific research.

This toolkit provides comprehensive processing and analysis capabilities for various physiological biosignals, including ECG, EEG, EDA, PPG, and EMG. It is designed for advanced psychophysiology research, clinical diagnosis, and human-computer interaction studies, supporting complex analyses like HRV, microstate detection, and autonomic assessment.

An end-to-end toolkit for analyzing Neuropixels high-density neural recordings, covering the entire electrophysiology workflow. Features include loading diverse formats (SpikeGLX, NWB), preprocessing, motion/drift correction, advanced spike sorting (Kilosort4, etc.), quality metric calculation, and unit curation (AI-assisted and model-based). Ideal for researchers working with multi-shank, high-channel count neural data.

PathML is a comprehensive Python toolkit designed for advanced computational pathology workflows. It facilitates the end-to-end analysis of whole-slide pathology images (WSI) from over 160 proprietary formats. Key functionalities include image preprocessing (stain normalization, tissue detection), constructing spatial cell/tissue graphs, and training deep learning models (e.g., HoVer-Net). It is highly specialized for analyzing multiparametric data from technologies like CODEX and Vectra.

This comprehensive toolkit facilitates the entire phylogenetic analysis workflow, from sequence alignment to tree inference. It integrates industry-standard tools like MAFFT for multiple sequence alignment, TrimAl for alignment trimming, and powerful maximum likelihood methods using IQ-TREE 2 or FastTree. Ideal for molecular genomics, tracing viral phylodynamics, studying protein evolution, or conducting molecular clock analysis. Built using Python for seamless integration.

PyDESeq2 is a robust Python implementation of the DESeq2 method, designed for analyzing differential gene expression in bulk RNA-seq count data. It supports complete bioinformatics workflows, including data loading, multi-factor design specification (e.g., batch effects, covariates), normalization (size factors), dispersion shrinkage, and rigorous statistical testing (Wald tests with FDR correction). Ideal for converting R-based DESeq2 pipelines into scalable, Python-native analysis pipelines.

A powerful Python library for reading, writing, and manipulating DICOM files, the standard format for medical imaging data. Use it for extracting pixel data from various scans (CT, MRI, X-ray), managing complex metadata tags, handling data compression, performing patient data anonymization, and converting medical images to standard formats for research and clinical applications.

Pymatgen is a comprehensive Python library designed for computational materials science. It empowers users to create, manipulate, and analyze crystal structures and molecular systems. Key functionalities include calculating phase diagrams, analyzing electronic structures (Density of States, band gaps), performing symmetry analysis, generating supercells, and accessing data from the Materials Project database. It supports over 100 file formats from various computational codes.