Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review

Author(s)

Zhang, Xue ^[1] ; Acencio, Marcio Luis ^{[2, 3]} ; Lemke, Ney ^[2]

Affiliation

^[1] Xiangnan Univ, Dept Comp Sci, Chenzhou, Hunan - Peoples R China

^[2] Sao Paulo State Univ, Inst Biosci Botucatu, Dept Phys & Biophys, Botucatu, SP - Brazil

^[3] Norwegian Univ Sci & Technol, Dept Canc Res & Mol Med, Fac Med, N-7034 Trondheim - Norway

• Document type: Review article
• Source: FRONTIERS IN PHYSIOLOGY; v. 7, MAR 8 2016.
• Web of Science Citations: 21

Abstract

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. (AU)

• FAPESP's process: 13/02018-4 - Machine learning for molecular systems biology (MLMSB) application on synthetic lethality, conditionally essential genes and cooperative transcription
• Grantee: Ney Lemke
• Support Opportunities: Regular Research Grants