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

Lester James Miranda, Takayuki Furuzuki

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

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018
EditorsChung-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
PublisherIEEE Computer Society
Pages480-485
Number of pages6
Volume1
ISBN (Electronic)9781538626665
DOIs
Publication statusPublished - 2018 Jun 8
Event42nd IEEE Computer Software and Applications Conference, COMPSAC 2018 - Tokyo, Japan
Duration: 2018 Jul 232018 Jul 27

Other

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

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Keywords

  • Artificial intelligence
  • Bioinformatics
  • Feature extraction
  • Machine learning
  • Medical computing
  • Multi-label classification

ASJC Scopus subject areas

  • Software
  • Computer Science Applications

Cite this

Miranda, L. J., & Furuzuki, T. (2018). A Deep Learning Approach Based on Stacked Denoising Autoencoders for Protein Function Prediction. In C-H. Lung, T. Conte, L. Liu, T. Akiyama, K. Hasan, E. Tovar, H. Takakura, W. Claycomb, S. Cimato, J-J. Yang, Z. Zhang, S. I. Ahamed, S. Reisman, C. Demartini, & M. Nakamura (Eds.), Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018 (Vol. 1, pp. 480-485). [8377699] IEEE Computer Society. https://doi.org/10.1109/COMPSAC.2018.00074