DIFFUSE: predicting isoform functions from sequences and expression profiles via deep learning.

Journal: Bioinformatics (Oxford, England)
Published Date: Jul 15, 2019

Abstract

MOTIVATION: Alternative splicing generates multiple isoforms from a single gene, greatly increasing the functional diversity of a genome. Although gene functions have been well studied, little is known about the specific functions of isoforms, making accurate prediction of isoform functions highly desirable. However, the existing approaches to predicting isoform functions are far from satisfactory due to at least two reasons: (i) unlike genes, isoform-level functional annotations are scarce. (ii) The information of isoform functions is concealed in various types of data including isoform sequences, co-expression relationship among isoforms, etc.

Authors

  • Hao Chen
    The First School of Medicine, Wenzhou Medical University, Wenzhou, China.
  • Dipan Shaw
    Department of Compute Science and Engineering, University of California, Riverside, CA, USA.
  • Jianyang Zeng
    Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China; MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China. Electronic address: zengjy321@tsinghua.edu.cn.
  • Dongbo Bu
    Key Lab of Intelligent Information Process, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
  • Tao Jiang
    Department of Respiratory and Critical Care Medicine, Center for Respiratory Medicine, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China.

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