Literature DB >> 30776072

Network-based methods for predicting essential genes or proteins: a survey.

Xingyi Li1, Wenkai Li1, Min Zeng1, Ruiqing Zheng1, Min Li1.   

Abstract

Genes that are thought to be critical for the survival of organisms or cells are called essential genes. The prediction of essential genes and their products (essential proteins) is of great value in exploring the mechanism of complex diseases, the study of the minimal required genome for living cells and the development of new drug targets. As laboratory methods are often complicated, costly and time-consuming, a great many of computational methods have been proposed to identify essential genes/proteins from the perspective of the network level with the in-depth understanding of network biology and the rapid development of biotechnologies. Through analyzing the topological characteristics of essential genes/proteins in protein-protein interaction networks (PINs), integrating biological information and considering the dynamic features of PINs, network-based methods have been proved to be effective in the identification of essential genes/proteins. In this paper, we survey the advanced methods for network-based prediction of essential genes/proteins and present the challenges and directions for future research.
© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Keywords:  biological information; dynamic features; essential genes/proteins; network-based methods; topological characteristics

Year:  2020        PMID: 30776072     DOI: 10.1093/bib/bbz017

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  14 in total

1.  IHP-PING-generating integrated human protein-protein interaction networks on-the-fly.

Authors:  Gaston K Mazandu; Christopher Hooper; Kenneth Opap; Funmilayo Makinde; Victoria Nembaware; Nicholas E Thomford; Emile R Chimusa; Ambroise Wonkam; Nicola J Mulder
Journal:  Brief Bioinform       Date:  2021-07-20       Impact factor: 11.622

2.  ProB-Site: Protein Binding Site Prediction Using Local Features.

Authors:  Sharzil Haris Khan; Hilal Tayara; Kil To Chong
Journal:  Cells       Date:  2022-07-05       Impact factor: 7.666

3.  Inference of Gene Regulatory Network from Single-Cell Transcriptomic Data Using pySCENIC.

Authors:  Nilesh Kumar; Bharat Mishra; Mohammad Athar; Shahid Mukhtar
Journal:  Methods Mol Biol       Date:  2021

4.  On the relation of gene essentiality to intron structure: a computational and deep learning approach.

Authors:  Ethan Schonfeld; Edward Vendrow; Joshua Vendrow; Elan Schonfeld
Journal:  Life Sci Alliance       Date:  2021-04-27

5.  Supervised learning is an accurate method for network-based gene classification.

Authors:  Renming Liu; Christopher A Mancuso; Anna Yannakopoulos; Kayla A Johnson; Arjun Krishnan
Journal:  Bioinformatics       Date:  2020-06-01       Impact factor: 6.937

6.  Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling.

Authors:  Wenying Yan; Xingyi Liu; Yibo Wang; Shuqing Han; Fan Wang; Xin Liu; Fei Xiao; Guang Hu
Journal:  Front Pharmacol       Date:  2020-04-30       Impact factor: 5.810

7.  Computer aided analysis of disease linked protein networks.

Authors:  Soudabeh Sabetian; Mohd Shahir Shamsir
Journal:  Bioinformation       Date:  2019-07-31

8.  Essential gene prediction using limited gene essentiality information-An integrative semi-supervised machine learning strategy.

Authors:  Sutanu Nandi; Piyali Ganguli; Ram Rup Sarkar
Journal:  PLoS One       Date:  2020-11-30       Impact factor: 3.240

9.  DeepHE: Accurately predicting human essential genes based on deep learning.

Authors:  Xue Zhang; Wangxin Xiao; Weijia Xiao
Journal:  PLoS Comput Biol       Date:  2020-09-16       Impact factor: 4.475

10.  Robustness and lethality in multilayer biological molecular networks.

Authors:  Xueming Liu; Enrico Maiorino; Arda Halu; Kimberly Glass; Rashmi B Prasad; Joseph Loscalzo; Jianxi Gao; Amitabh Sharma
Journal:  Nat Commun       Date:  2020-11-27       Impact factor: 14.919

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