PDF(Pubmed)
Abstract：
BACKGROUND: 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 from genetic data.
METHODS: We propose a Quantum Neural Networks architecture to discover genetic biomarkers for input activation pathways. The Maximum Relevance-Minimum Redundancy criteria score biomarker candidate sets. Our proposed model is economical since the neural solution can be delivered on constrained hardware.
RESULTS: 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.
CONCLUSIONS: The model indicates new genetic 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 .
METHODS: We propose a Quantum Neural Networks architecture to discover genetic biomarkers for input activation pathways. The Maximum Relevance-Minimum Redundancy criteria score biomarker candidate sets. Our proposed model is economical since the neural solution can be delivered on constrained hardware.
RESULTS: 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.
CONCLUSIONS: The model indicates new genetic 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 .
摘要：
背景：由于巨大的搜索空间，生物标志物的发现是一项具有挑战性的任务。量子计算和量子人工智能（量子AI）可用于解决从遗传数据中发现生物标志物的计算问题。
方法：我们提出了一种量子神经网络架构来发现输入激活途径的遗传生物标志物。最大相关性-最小冗余标准评分生物标志物候选集。我们提出的模型是经济的，因为神经解决方案可以在受约束的硬件上交付。
结果：我们证明了与CTLA4相关的四种激活途径的概念证明，包括（1）CTLA4激活独立，（2）CTLA4-CD8A-CD8B共激活，（3）CTLA4-CD2共激活，和(4)CTLA4-CD2-CD48-CD53-CD58-CD84共激活。
结论：该模型表明与CLTA4相关途径的突变激活相关的新遗传生物标志物，包括20个基因：CLIC4，CPE，ETS2,FAM107A,GPR116,HYOU1,LCN2,MACF1,MT1G,NAPA,NDUFS5,PAK1,PFN1,PGAP3,PPM1G,PSMD8、RNF213、SLC25A3、UBA1和WLS。我们开源实现：https://github.com/namnguyen0510/Biomarker-Discovery-with-Quantum-Neural-Networks。
方法：我们提出了一种量子神经网络架构来发现输入激活途径的遗传生物标志物。最大相关性-最小冗余标准评分生物标志物候选集。我们提出的模型是经济的，因为神经解决方案可以在受约束的硬件上交付。
结果：我们证明了与CTLA4相关的四种激活途径的概念证明，包括（1）CTLA4激活独立，（2）CTLA4-CD8A-CD8B共激活，（3）CTLA4-CD2共激活，和(4)CTLA4-CD2-CD48-CD53-CD58-CD84共激活。
结论：该模型表明与CLTA4相关途径的突变激活相关的新遗传生物标志物，包括20个基因：CLIC4，CPE，ETS2,FAM107A,GPR116,HYOU1,LCN2,MACF1,MT1G,NAPA,NDUFS5,PAK1,PFN1,PGAP3,PPM1G,PSMD8、RNF213、SLC25A3、UBA1和WLS。我们开源实现：https://github.com/namnguyen0510/Biomarker-Discovery-with-Quantum-Neural-Networks。
