A disease-related essential protein prediction model based on the transfer neural network

被引:2
作者
Chen, Sisi [1 ]
Huang, Chiguo [2 ]
Wang, Lei [1 ,2 ]
Zhou, Shunxian [1 ,2 ,3 ]
机构
[1] Hunan Univ Chinese Med, Hosp 1, Changsha, Hunan, Peoples R China
[2] Changsha Univ, Big Data Innovat & Entrepreneurship Educ Ctr Hunan, Changsha, Peoples R China
[3] Hunan Womens Univ, Coll Informat Sci & Engn, Changsha, Hunan, Peoples R China
基金
中国国家自然科学基金;
关键词
essential protein; prediction model; transfer neural network; biological information; internal disease; protein-protein interaction network; CENTRALITY; IDENTIFICATION; TOPOLOGY; DATABASE;
D O I
10.3389/fgene.2022.1087294
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Essential proteins play important roles in the development and survival of organisms whose mutations are proven to be the drivers of common internal diseases having higher prevalence rates. Due to high costs of traditional biological experiments, an improved Transfer Neural Network (TNN) was designed to extract raw features from multiple biological information of proteins first, and then, based on the newly-constructed Transfer Neural Network, a novel computational model called TNNM was designed to infer essential proteins in this paper. Different from traditional Markov chain, since Transfer Neural Network adopted the gradient descent algorithm to automatically obtain the transition probability matrix, the prediction accuracy of TNNM was greatly improved. Moreover, additional antecedent memory coefficient and bias term were introduced in Transfer Neural Network, which further enhanced both the robustness and the non-linear expression ability of TNNM as well. Finally, in order to evaluate the identification performance of TNNM, intensive experiments have been executed based on two well-known public databases separately, and experimental results show that TNNM can achieve better performance than representative state-of-the-art prediction models in terms of both predictive accuracies and decline rate of accuracies. Therefore, TNNM may play an important role in key protein prediction in the future.
引用
收藏
页数:12
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