A Deep Learning Approach Based on Stacked Denoising Autoencoders for Protein Function Prediction

Lester James Miranda, Takayuki Furuzuki

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

Predicting protein functions is a fundamental task with applications in medicine and healthcare. However, the accelerating pace of protein-discovery renders slow and expensive biochemical techniques unsustainable. Machine learning is suitable for such data-intensive task, but the presence of noise in protein datasets adds another level of difficulty. Hence, we propose a deep learning system based on a stacked denoising autoencoder that extracts robust features to improve predictive performance. We then feed the resulting features to a multilabel support-vector machine for classification. We evaluated on two protein benchmarks, and experimental results show that our system consistently produced the best performance against techniques that do not have a denoising or feature learning capability. This research demonstrates that learning robust representations from raw data can benefit the process of predicting protein functions.

本文言語English
ホスト出版物のタイトルProceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018
編集者Chung-Horng Lung, Thomas Conte, Ling Liu, Toyokazu Akiyama, Kamrul Hasan, Edmundo Tovar, Hiroki Takakura, William Claycomb, Stelvio Cimato, Ji-Jiang Yang, Zhiyong Zhang, Sheikh Iqbal Ahamed, Sorel Reisman, Claudio Demartini, Motonori Nakamura
出版社IEEE Computer Society
ページ480-485
ページ数6
1
ISBN(電子版)9781538626665
DOI
出版ステータスPublished - 2018 6 8
イベント42nd IEEE Computer Software and Applications Conference, COMPSAC 2018 - Tokyo, Japan
継続期間: 2018 7 232018 7 27

Other

Other42nd IEEE Computer Software and Applications Conference, COMPSAC 2018
CountryJapan
CityTokyo
Period18/7/2318/7/27

ASJC Scopus subject areas

  • Software
  • Computer Science Applications

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