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基于量子神经网络的生物标志物发现：以 CTLA4 激活途径为例。

Biomarker discovery with quantum neural networks: a case-study in CTLA4-activation pathways.

机构信息

Faculty of Computer Science, PHENIKAA University, Yen Nghia, Ha Dong, Hanoi, 12116, Vietnam.

出版信息

BMC Bioinformatics. 2024 Apr 12;25(1):149. doi: 10.1186/s12859-024-05755-0.

DOI:10.1186/s12859-024-05755-0
PMID:38609844
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11265126/
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. METHOD: 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. CONCLUSION: 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：由于搜索空间巨大，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（quantum 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，用于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。由于可以在受限的硬件上提供神经解决方案，因此我们的模型具有经济性。

结果：我们在与 CTLA4 相关的四个激活途径（包括 1）CTLA4 激活独立途径、2）CTLA4-CD8A-CD8B 共激活途径、3）CTLA4-CD2 共激活途径和 4）CTLA4-CD2-CD48-CD53-CD58-CD84 共激活途径上证明了该概念的可行性。

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、PPMD8、RNF213、SLC25A3、UBA1 和 WLS。我们在 https://github.com/namnguyen0510/Biomarker-Discovery-with-Quantum-Neural-Networks 上开源了实现。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4adf/11265126/3ca6c704391b/12859_2024_5755_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4adf/11265126/60f8bd1f831f/12859_2024_5755_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4adf/11265126/482b52de9c7f/12859_2024_5755_Figa_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4adf/11265126/3ca6c704391b/12859_2024_5755_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4adf/11265126/60f8bd1f831f/12859_2024_5755_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4adf/11265126/482b52de9c7f/12859_2024_5755_Figa_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4adf/11265126/3ca6c704391b/12859_2024_5755_Fig2_HTML.jpg

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本文引用的文献

[1]
IntelliGenes: a novel machine learning pipeline for biomarker discovery and predictive analysis using multi-genomic profiles.

Bioinformatics. 2023-12-1

[2]
Single cell dynamics of tumor specificity vs bystander activity in CD8 T cells define the diverse immune landscapes in colorectal cancer.

Cell Discov. 2023-11-15

[3]
Future Potential of Quantum Computing and Simulations in Biological Science.

Mol Biotechnol. 2024-9

[4]
Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics.

Mol Biotechnol. 2024-2

[5]
GPR116 receptor regulates the antitumor function of NK cells via Gαq/HIF1α/NF-κB signaling pathway as a potential immune checkpoint.

Cell Biosci. 2023-3-9

[6]
The Expression Pattern of Adhesion G Protein-Coupled Receptor F5 Is Related to Cell Adhesion and Metastatic Pathways in Colorectal Cancer-Comprehensive Study Based on In Silico Analysis.

Cells. 2022-12-1

[7]
Insights from incorporating quantum computing into drug design workflows.

Bioinformatics. 2023-1-1

[8]
Quantum computing algorithms: getting closer to critical problems in computational biology.

Brief Bioinform. 2022-11-19

[9]
Ten quick tips for biomarker discovery and validation analyses using machine learning.

PLoS Comput Biol. 2022-8-11

[10]
Landscape of somatic alterations in large-scale solid tumors from an Asian population.

Nat Commun. 2022-7-23

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