Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review.
Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review.
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基于机器学习和网络拓扑特征预测必需基因和蛋白质：综合综述
DOI：
10.3389/fphys.2016.00075
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发表时间：
2016
影响因子：
4
通讯作者：
Lemke N
中科院分区：
文献类型：
--
作者：
Zhang X;Acencio ML;Lemke N
必需蛋白质/基因是生物体生存或繁殖所不可或缺的，这些必需蛋白质的缺失将导致死亡或不育。必需基因的鉴定不仅对于了解生物体生存的最低要求非常重要，而且对于发现人类疾病基因和新的药物靶点也非常重要。用于鉴定必需基因的实验方法是昂贵的、耗时的和费力的。随着基因组测序数据和高通量实验数据的积累，提出了许多识别必需蛋白质的计算方法，这些方法是对实验方法的有益补充。本文综述了基于机器学习和网络拓扑特征的必需基因和蛋白质识别方法，指出了现有方法的进展和局限性，并讨论了进一步研究的挑战和方向。
Essential proteins/genes are indispensable to the survival or reproduction of an organism, and the deletion of such essential proteins will result in lethality or infertility. The identification of essential genes is very important not only for understanding the minimal requirements for survival of an organism, but also for finding human disease genes and new drug targets. Experimental methods for identifying essential genes are costly, time-consuming, and laborious. With the accumulation of sequenced genomes data and high-throughput experimental data, many computational methods for identifying essential proteins are proposed, which are useful complements to experimental methods. In this review, we show the state-of-the-art methods for identifying essential genes and proteins based on machine learning and network topological features, point out the progress and limitations of current methods, and discuss the challenges and directions for further research.