Stars
An R package to perform differential network analysis in single and multi-omic data. It can also be used for feature selection in ML.
Interactive spatial transcriptomics viewer for h5ad files
A Python library to work with, analyze, filter and inspect the Human Phenotype Ontology
A computational framework for modelling intrinsic and extrinsic factors driving cell plasticity using spatial transcriptomics data, graph neural networks (GNNs) and geostatistical regression.
Code for embedding human phenotype ontology (HPO) using Node2Vec and incorporating phenotype frequencies from a hospital-size dataset. Showcased in the linked paper.
Diffprivlib: The IBM Differential Privacy Library
Modelling human health trajectories using generative transformers
Genomic Pretrained Network - GPN, GPN-MSA, PhyloGPN, GPN-Star
daifengwanglab / COSIME
Forked from jeromejchoi/COSIMECOSIME: Cooperative multi-view integration and Scalable and Interpretable Model Explainer
Python package for the semi-supervised analysis of multi-omics data
This API provides programmatic access to the AlphaGenome model developed by Google DeepMind.
A toolkit for training deep learning models on genotype, tabular, sequence, image, array and binary data.
Complete API layer for private AI applications on local models: RAG, skills, tools, MCP, text-to-sql, and more. Works with any OpenAI-compatible inference server.
Open-source foundation models for multi-omics representation learning
An autonomous agent that conducts deep research on any data using any LLM providers
Supporting material for publication: "Ovarian cancer mutational processes drive site-specific immune evasion"
Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics
Code repository associated with the paper "Tissue structure modulates evolution: premalignant sweeps precede neutral expansion"
A unifying representation of single cell expression profiles that quantifies similarity between expression states and generalizes to represent new studies without additional training.
BaCaDI: Bayesian Causal Discovery with Unknown Interventions
Reference-free cell-type deconvolution of multi-cellular spatially resolved transcriptomics data