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

Journal: Briefings in bioinformatics
Published Date:

Abstract

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.

Authors

  • Jiajie Peng
    School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. jiajiepeng@hit.edu.cn.
  • Hansheng Xue
    School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
  • Zhongyu Wei
    School of Data Science, Fudan University, Shanghai, China.
  • Idil Tuncali
    School of Data Science, Fudan University, Shanghai, 200433, China.
  • Jianye Hao
    College of Intelligence and Computing, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350, China. haojianye@gmail.com.
  • Xuequn Shang