Single-cell multi-omics integration using Optimal Transport

A python framework for microbial natural products data mining by integrating genomics and metabolomics data

PathIntegrate Python package for pathway-based multi-omics data integration

MOGONET (Multi-Omics Graph cOnvolutional NETworks) is multi-omics data integrative analysis framework for classification tasks in biomedical applications.

COSMOS is a computational tool crafted to overcome the challenges associated with integrating spatially resolved multi-omics data. This software harnesses a graph neural network algorithm to deliver cutting-edge solutions for analyzing biological data that encompasses various omics types within a spatial framework.

Multimodal Integration with Modality-agnostic Autoencoders - Developed by LMIB @ KU Leuven

A modular Nextflow pipeline for multi-omics integration, survival machine learning, and automated reporting.

SMART is a computational framework for spatial multi-omic integration. SMART leverages a modality-independent modular and stacking framework with spatial coordinates and adjusts the aggregation using triplet relationships.

Computational pipeline for novel IRD gene discovery — HPO phenotypic clustering, evolutionary profiling (NPP), and multi-omics integration. M.Sc. research, Hebrew University.

Joint analysis single-cell and spatial transcriptomics data with stSCI

Geospatial Factors Shape the Human Brain-Gut-Microbiome System Minimal Reproducible Analysis: This repository ships a compact, execution‑ready subset of code to reproduce the manuscript’s core statistical findings: dose–response screens, cross‑modal integration (sPLS‑DA/DIABLO), and a publication‑style volcano plot.

NetICS: network-based integration of multi-omics data for prioritizing cancer genes

An nf-core pipeline for epigenome segmentation using EpiSegMix/Meth — a hidden Markov model with flexible read count distributions and state duration modeling for histone, open chromatin, and methylation signals.

End-to-end computational platform for pharmaceutical-grade cancer biomarker discovery. Integrates multi-omics data, machine learning, and clinical validation frameworks for precision oncology applications.

Code repository for "MyeVAE: A multi-omics variational autoencoder for predicting mortality in newly diagnosed myeloma patients"

A multiomics variational autoencoder for risk prediction in newly diagnosed multiple myeloma

Project from workshop "Introduction to multi-omics data integration and visualisation" @embl-ebi

Complete scripts used to produce the results of Imperial MRes Biomedical Research - Data Science 2024-2025 Project 1.

Integration of metagenomics and metabolomics data to explore the dynamics during the alcoholic fermentation process

Interpretable multimodal neural network framework that integrates single-cell and spatial omics through biologically constrained, concept-bottleneck architectures.