Skill UI

Provides GraphQL queries to the Open Targets Platform for target-disease associations, tractability, safety, drugs, and multi-omics evidence to guide therapeutic target identification, safety assessment, and repurposing research workflows.

Probabilistic deep-learning framework built on PyTorch for single-cell omics; handles batch correction, integration, multimodal and spatial data, differential expression, and transfer learning with unified APIs.

Access multi-dimensional cancer genomics data via cBioPortal REST API for mutation, copy-number, expression, and survival insights across TCGA/GENIE studies, enabling target validation and clinical association analyses programmatically with Python clients.

A comprehensive toolkit for reconstructing and analyzing the evolutionary history of biological sequences (genes, proteins, genomes). The standard workflow includes multiple sequence alignment using MAFFT, robust maximum likelihood tree inference with IQ-TREE 2 and FastTree, and advanced visualization/manipulation using ETE3. This skill is essential for studies in viral phylodynamics, microbial genomics, protein family evolution, and molecular clock dating.

PathML is a comprehensive Python framework designed for computational pathology workflows. It supports end-to-end analysis of Whole Slide Images (WSI), including loading 160+ proprietary formats, performing advanced stain normalization and tissue segmentation, constructing spatial cell graphs, and training deep learning models (e.g., HoVer-Net) for multiplex immunofluorescence and spatial proteomics. Ideal for large-scale biomedical image analysis.

This comprehensive skill provides a standardized pipeline for reconstructing the evolutionary history of biological sequences (genes, proteins, genomes). It guides users through essential steps, including multiple sequence alignment using MAFFT, maximum likelihood tree inference with IQ-TREE 2, and visualization with ETE3. Ideal for research in viral phylodynamics, molecular clock studies, and large-scale comparative genomics.