Feature Extraction Using a Mutually-Competitive Autoencoder for Protein Function Prediction

Lester James Miranda, Jinglu Hu

研究成果: Conference contribution

抄録

Learning new representations from data has been effective in predicting protein functions. However, common techniques tend to extract features irrelevant to the classification task. We propose an autoencoder network that selectively extracts features to produce meaningful representations. By increasing the activation of neurons kept by a winner-take-all operation, hidden units compete to form a subset that encodes relevant features, a process dubbed as mutual competition. We test this method on protein benchmarks, evaluating feature score distribution and classification performance. Results show that the autoencoder extracted features relevant to the classification task, and significantly outperformed other techniques in literature based on non-parameteric statistical tests. This demonstrates that adding competition between neurons encodes meaningful features, further improving the prediction of protein functions.

本文言語English
ホスト出版物のタイトルProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1337-1342
ページ数6
ISBN(電子版)9781538666500
DOI
出版ステータスPublished - 2019 1 16
イベント2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan
継続期間: 2018 10 72018 10 10

出版物シリーズ

名前Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

Conference

Conference2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
国/地域Japan
CityMiyazaki
Period18/10/718/10/10

ASJC Scopus subject areas

  • 情報システム
  • 情報システムおよび情報管理
  • 健康情報学
  • 人工知能
  • コンピュータ ネットワークおよび通信
  • 人間とコンピュータの相互作用

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