A deep neural network based hierarchical multi-label classifier for protein function prediction

Xin Yuan, Weite Li, Kui Lin, Jinglu Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Hierarchical classification approaches have been shown to be effective for protein function prediction problem. Traditionally, a set of simple classifiers are used for each hierarchical level separately. In this paper, we introduce a deep neural network (DNN) model with multiple heads and multiple ends to realize the whole set of classifiers. The DNN model consists of three parts: the body part, the multi-end part and the multi-head part. The body part is a deep multilayer perceptron (MLP) shared by different classifiers for feature mapping. The multi-end part performs feature fusion transforming the input vectors of different classifiers to feature vectors with the same length so as to share the feature mapping part. The multi-head part is a set of linear multi-label classifiers. By sharing a deep MLP with multiple classifiers, we are able to construct more powerful classifiers for each level with limited training samples, and expecting to have better classification performance. Experiment results on benchmark datasets show that the proposed method significantly outperforms these traditional methods.

Original languageEnglish
Title of host publicationCITS 2019 - Proceeding of the 2019 International Conference on Computer, Information and Telecommunication Systems
EditorsMohammad S. Obaidat, Zhenqiang Mi, Kuei-Fang Hsiao, Petros Nicopolitidis, Daniel Cascado-Caballero
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538640883
DOIs
Publication statusPublished - 2019 Aug
Event2019 International Conference on Computer, Information and Telecommunication Systems, CITS 2019 - Beijing, China
Duration: 2019 Aug 282019 Aug 31

Publication series

NameCITS 2019 - Proceeding of the 2019 International Conference on Computer, Information and Telecommunication Systems

Conference

Conference2019 International Conference on Computer, Information and Telecommunication Systems, CITS 2019
CountryChina
CityBeijing
Period19/8/2819/8/31

Keywords

  • Auto-Encoder
  • Deep Neural Network
  • Feature Fusion
  • Hierarchical Multi-Label Classification
  • Protein Function Prediction

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications
  • Hardware and Architecture

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