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一个知识图的方法来预测和解释引起疾病的基因相互作用

Alexandre Renaux^1,2,3, Chloé Terwagne^4,5, Michael Cochez^6,7

¹Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles - Vrije Universiteit Brussel, Brussels, Belgium. Alexandre.Renaux@ulb.be.

BMC bioinformatics

|August 29, 2023

概括

这项研究引入了一种新的知识图方法,用于预测引起疾病的基因相互作用. 该方法提供了准确的预测和清晰的解释,提高对寡生性疾病的理解.

科学领域:

遗传学遗传学是一个
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

了解基因相互作用对于遗传疾病研究至关重要,特别是对由多个基因变异影响的寡原性疾病至关重要.
目前用于识别寡原性疾病中的基因组合的机器学习方法缺乏可解释性,阻碍了临床验证.
需要有预测方法,为引起疾病的基因相互作用提供透明的解释.

研究的目的:

开发一种新的,可解释的预测方法来识别引起疾病的基因相互作用.
提供精确的预测致病基因相互作用在寡原性疾病.
为预测生成解释,帮助医学专家验证和理解.

主要方法:

构建的BOCK,一个知识图集临床数据,生物医学网络和对寡生性疾病的本体学.
开发了一个使用知识图中的异质路径来连接基因对的预测框架.
训练了一种可解释的决策集模型,以预测致病基因相互作用并识别相关模式.

主要成果:

本书的知识图表有助于探索引起疾病的遗传相互作用.
预测框架准确地识别了致病基因相互作用和相关的疾病模式.
该方法为每个预测提供子图解释,详细说明使用的证据.

结论:

开发的方法利用知识图和异质路径进行可解释的病原性基因相互作用预测.
这种方法增强了对寡生性疾病中的分子机制的理解.
它代表了知识图的新应用,用于透明和有洞察力的遗传研究预测器.

关键词:

疾病遗传学疾病遗传学遗传相互作用遗传相互作用可解释的机器学习.知识图是知识图.

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