Integrating multi-network topology for gene function prediction using deep neural networks

Jiajie Peng, Hansheng Xue, Zhongyu Wei, Idil Tuncali, Jianye Hao, Xuequn Shang

科研成果: 期刊稿件文章同行评审

66 引用 (Scopus)

摘要

Motivation: The emergence of abundant biological networks, which benefit from the development of advanced high-throughput techniques, contributes to describing and modeling complex internal interactions among biological entities such as genes and proteins. Multiple networks provide rich information for inferring the function of genes or proteins. To extract functional patterns of genes based on multiple heterogeneous networks, network embedding-based methods, aiming to capture non-linear and low-dimensional feature representation based on network biology, have recently achieved remarkable performance in gene function prediction. However, existing methods do not consider the shared information among different networks during the feature learning process. Results: Taking the correlation among the networks into account, we design a novel semi-supervised autoencoder method to integrate multiple networks and generate a low-dimensional feature representation. Then we utilize a convolutional neural network based on the integrated feature embedding to annotate unlabeled gene functions. We test our method on both yeast and human datasets and compare with three state-of-the-art methods. The results demonstrate the superior performance of our method. We not only provide a comprehensive analysis of the performance of the newly proposed algorithm but also provide a tool for extracting features of genes based on multiple networks, which can be used in the downstream machine learning task.

源语言英语
页(从-至)2096-2105
页数10
期刊Briefings in Bioinformatics
22
2
DOI
出版状态已出版 - 1 3月 2021

指纹

探究 'Integrating multi-network topology for gene function prediction using deep neural networks' 的科研主题。它们共同构成独一无二的指纹。

引用此