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Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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A Network-Based Framework to Discover Treatment-Response-Predicting Biomarkers for Complex Diseases.

Uday S Shanthamallu¹, Casey Kilpatrick², Alex Jones¹

¹Department of Data Science and Network Medicine, Scipher Medicine, Waltham, Massachusetts.

The Journal of Molecular Diagnostics : JMD

|July 27, 2024

Summary

Precision medicine for autoimmune diseases faces data challenges. A new network medicine framework, PRoBeNet, identifies predictive biomarkers, improving machine learning models with limited patient data.

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Area of Science:

Biomedical Informatics
Network Medicine
Precision Medicine

Background:

Precision medicine for complex autoimmune diseases is hindered by limited data and high-dimensional omics data.
Developing effective treatments requires robust biomarkers for patient stratification.

Purpose of the Study:

To introduce PRoBeNet (Predictive Response Biomarkers using Network medicine), a novel framework to identify predictive biomarkers for autoimmune disease therapies.
To address data limitations in high-throughput multi-omics studies for precision medicine.

Main Methods:

PRoBeNet integrates therapy-targeted proteins, disease signatures, and the human interactome network.
Biomarker discovery and validation using retrospective and prospective patient data (ulcerative colitis, rheumatoid arthritis, Crohn disease).
Machine learning models were built using PRoBeNet biomarkers, all genes, or random genes.

Main Results:

PRoBeNet identified biomarkers predicting response to infliximab and a MAPK3/1 inhibitor.
Models using PRoBeNet biomarkers significantly outperformed others, especially with limited data.
Validated predictive biomarkers in ulcerative colitis, rheumatoid arthritis, and Crohn disease patient cohorts.

Conclusions:

PRoBeNet effectively reduces feature dimensionality and builds robust machine learning models for precision medicine, even with scarce data.
PRoBeNet facilitates the development of diagnostic assays for clinical trial stratification and improved patient outcomes in autoimmune diseases.