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Predicting disease‐associated genes: Computational methods, databases, and evaluations
WIREs Data Mining and Knowledge Discovery ( IF 7.8 ) Pub Date : 2020-07-28 , DOI: 10.1002/widm.1383
Ping Luo 1, 2 , Bolin Chen 3 , Bo Liao 4 , Fang‐Xiang Wu 5
Affiliation  

Complex diseases are associated with a set of genes (called disease genes), the identification of which can help scientists uncover the mechanisms of diseases and develop new drugs and treatment strategies. Due to the huge cost and time of experimental identification techniques, many computational algorithms have been proposed to predict disease genes. Although several review publications in recent years have discussed many computational methods, some of them focus on cancer driver genes while others focus on biomolecular networks, which only cover a specific aspect of existing methods. In this review, we summarize existing methods and classify them into three categories based on their rationales. Then, the algorithms, biological data, and evaluation methods used in the computational prediction are discussed. Finally, we highlight the limitations of existing methods and point out some future directions for improving these algorithms. This review could help investigators understand the principles of existing methods, and thus develop new methods to advance the computational prediction of disease genes.

中文翻译:

预测疾病相关基因:计算方法,数据库和评估

复杂的疾病与一组基因(称为疾病基因)相关,对它们的鉴定可以帮助科学家发现疾病的机理,开发新药和治疗策略。由于实验鉴定技术的巨大成本和时间,已经提出了许多计算算法来预测疾病基因。尽管近年来有一些综述出版物讨论了许多计算方法,但其中一些集中在癌症驱动基因上,而另一些集中在生物分子网络上,这些仅涵盖现有方法的特定方面。在这篇综述中,我们总结了现有方法,并根据其原理将其分为三类。然后,讨论了在计算预测中使用的算法,生物学数据和评估方法。最后,我们强调了现有方法的局限性,并指出了改进这些算法的未来方向。这项审查可以帮助研究人员了解现有方法的原理,从而开发新的方法来推进疾病基因的计算预测。
更新日期:2020-07-28
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