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:
this https URL

本研究使用量子计算和人工智能来解决生物标志物（biomarker）发现的计算问题，提出了一种量子神经网络的架构，经典的最大相关最小冗余（Maximum Relevance, Minimum Redundancy，mRMR）方法用于评估标志物潜在集。该模型标识了 20 个基因作为与 CTLA4 相关病理生物标志物的新标志物。