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通过使用其组织选择性表现来预测遗传性疾病的分子机制

Eyal Simonovsky¹, Moran Sharon¹, Maya Ziv¹

¹Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, Beer Sheva, Israel.

Molecular systems biology

|May 26, 2023

概括

一种新的机器学习方法,通过表达 (TRACE) 对因果关系的组织风险评估,可以识别组织选择性疾病基因. 通过分析基因表达模式,TRACE提高了对遗传性疾病的遗传和临床理解.

科学领域:

遗传学是一个遗传学.
计算生物学计算生物学
基因组学就是基因组学.

背景情况:

遗传性疾病往往是由广泛表达的基因中的遗传异常引起的.
了解组织特异性疾病机制至关重要,但具有挑战性.
以前用于识别与疾病相关的基因的方法范围有限.

研究的目的:

开发一个可扩展的机器学习方法来预测基因的基础组织选择性遗传性疾病.
识别疾病基因中与组织选择性相关的新特性.
创建一个全面的组织相关遗传风险目录.

主要方法:

开发了通过表达 (TRACE) 的因果关系组织风险评估机器学习模型.
利用了4,744个生物可解释的,来自omics数据的组织特异性基因特征.
将TRACE应用到1,031个已知的疾病基因和18,927个蛋白质编码基因的目录中.

主要成果:

确定了已知的和新的特征,有助于疾病中的组织选择性,包括以前被忽视的共同特征.
创建了18927个基因的组织相关风险目录.
在为罕见疾病患者优先选择候选疾病基因方面,TRACE显著优于现有方法.

结论:

组织选择性是理解广泛表达的基因引起的遗传性疾病的关键因素.
该TRACE框架增强了对罕见和遗传性疾病的遗传和临床理解.
机器学习与组织特异性基因特征的整合为疾病基因发现提供了强大的方法.

关键词:

数据整合数据集成.基因组医学是基因组医学.机器学习是机器学习.俄米克斯 (omicsics) 是一个电子产品.组织选择性组织选择性

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