Find Reactions, Proteins and Pathways
Pathway Browser
Visualize and interact with Reactome biological pathways
Analysis Tools
Merges pathway identifier mapping,
over-representation, and expression analysis
AI Chatbot
Meet the React-to-Me AI Chatbot! Designed to answer your questions about Reactome Pathways.
ReactomeFIViz
Designed to find pathways and network patterns related to cancer and other types of diseases
Documentation
Information to browse the database and use its principal tools for data analysis
Reactome Research Spotlight
[February 13, 2026] In their November 2025 NPJ Systems Biology and Applications paper “MarkerPredict: predicting clinically relevant predictive biomarkers with machine learning”, Veres et al. present MarkerPredict, a machine learning framework for identifying predictive biomarkers of targeted cancer therapies. The method integrates signaling network topology with protein disorder features, focusing on three-node motifs containing oncologic targets and intrinsically disordered proteins. ReactomeFI is used to define these motifs and shows the highest enrichment of IDP–target triangles among tested networks. MarkerPredict identified 2,084 candidate biomarkers, including LCK and ERK1, demonstrating the value of Reactome-derived network context for precision oncology.
Why Reactome
Reactome is a free, open-source, curated and peer-reviewed pathway database. Our goal is to provide intuitive bioinformatics tools for the visualization, interpretation and analysis of pathway knowledge to support basic research, genome analysis, modeling, systems biology and education.
The development of Reactome is supported by grants from the US National Institutes of Health (U24 HG012198) and the European Molecular Biology Laboratory.
Version 95 released on December 9, 2025
Human Pathways
Reactions
Proteins
Small Molecules
Drugs
Literature References
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API and Data access
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