Biomarker Discovery with Quantum Neural Networks: A Case-study in CTLA4-Activation Pathways

TL;DR

This paper introduces a quantum neural network approach for discovering biomarkers in CTLA4-related pathways, demonstrating its effectiveness on multiple activation pathways and identifying new gene biomarkers.

Contribution

The study presents a novel quantum neural network architecture for biomarker discovery, optimized for constrained hardware, and applies it to CTLA4 pathways to identify new biomarkers.

Findings

Identified 20 new gene biomarkers associated with CTLA4 pathways.

Demonstrated the model on four different CTLA4 activation pathways.

Provided open-source implementation for reproducibility.

Abstract

Biomarker discovery is a challenging task due to the massive search space. Quantum computing and quantum Artificial Intelligence (quantum AI) can be used to address the computational problem of biomarker discovery tasks. We propose a Quantum Neural Networks (QNNs) architecture to discover biomarkers for input activation pathways. The Maximum Relevance, Minimum Redundancy (mRMR) criteria is used to score biomarker candidate sets. Our proposed model is economical since the neural solution can be delivered on constrained hardware. We demonstrate the proof of concept on four activation pathways associated with CTLA4, including (1) CTLA4-activation stand-alone, (2) CTLA4-CD8A-CD8B co-activation, (3) CTLA4-CD2 co-activation, and (4) CTLA4-CD2-CD48-CD53-CD58-CD84 co-activation. The model indicates new biomarkers associated with the mutational activation of CLTA4-associated pathways, including 20 genes: CLIC4, CPE, ETS2, FAM107A, GPR116, HYOU1, LCN2, MACF1, MT1G, NAPA, NDUFS5, PAK1, PFN1, PGAP3, PPM1G, PSMD8, RNF213, SLC25A3, UBA1, and WLS. We open source the implementation at: https://github.com/namnguyen0510/Biomarker-Discovery-with-Quantum-Neural-Networks.