Otros
Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review
Date
2016-03-08Registration in:
Frontiers In Physiology. Lausanne: Frontiers Media Sa, v. 7, 11 p., 2016.
1664-042X
10.3389/fphys.2016.00075
WOS:000371564800001
WOS000371564800001.pdf
7977035910952141
Author
Xiangnan Univ
Universidade Estadual Paulista (Unesp)
Norwegian Univ Sci & Technol
Institutions
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.