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一个包括295个基因的综合目标小组:揭示多发性骨髓瘤中关键的疾病发起和转化生物标志物

Vivek Ruhela¹, Ritu Gupta², Rupin Oberoi³

¹SBILab, Deptt. of ECE & Centre of Excellence in Healthcare, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), India; Department of Computational Biology, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), India.

Computers in biology and medicine

|July 5, 2025

概括

这项研究介绍了BIO-DGI,这是用于多发性骨髓瘤 (MM) 生物标志物发现的AI框架. 它通过分析基因组变异和基因相互作用来识别早期MM检测的关键基因.

科学领域:

基因组学就是基因组学.
人工智能的人工智能
生物信息学是一种生物信息学.

背景情况:

多发性骨髓瘤 (MM) 是一种血液癌症,由未确定的意义单克隆性乳腺病变 (MGUS) 发展.
在早期检测和了解疾病发展方面,MM和MGUS之间的分子区别至关重要.
识别生物标志物,基因组改变和基因相互作用是MM病原体洞察的关键.

研究的目的:

开发一个人工智能驱动的框架,用于识别多重骨髓瘤生物标志物.
在分子层面上区分多发性骨髓瘤及其前体MGUS.
创建一个临床相关的基因面板用于多发性骨髓瘤检测.

主要方法:

开发了一个人工智能工作流程,将统计突变分析与基因-基因相互作用的深度学习模型 (BIO-DGI) 集成在一起.
从全外体和基因组测序数据中利用单核酸变异 (SNV),复制数变异 (CNV) 和结构变异 (SV).
进行了后期验证,包括ShAP分析,社区分析,生存分析和途径丰富.

主要成果:

生物-DGI模型在识别多个髓瘤相关基因方面表现优于传统方法.
后期分析导致了对多发性骨髓瘤295个基因组的设计.
途径丰富分析证实了该小组与MM相关的生物途径的强烈关联.

结论:

开发了一个综合框架,结合了生物启发的图形学习和多变体基因组分析.
新的BIO-DGI模型整合了外基因变异与基因相互作用.
对多发性骨髓瘤成功开发了一个临床策划的295基因小组.

关键词:

人工智能在癌症中的应用在癌症中的GCN.基因组的基因组.血液学的恶性瘤.这里是MGUS的MGUS.多发性骨髓瘤是一种多发性骨髓瘤.在 ShAP 分析中,

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