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

Carregando...
Imagem de Miniatura

Data

2016-03-08

Título da Revista

ISSN da Revista

Título de Volume

Editor

Frontiers Media Sa

Resumo

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.

Descrição

Palavras-chave

essential genes/proteins, machine learning, systems biology, prediction models, network topological features

Como citar

Frontiers In Physiology. Lausanne: Frontiers Media Sa, v. 7, 11 p., 2016.