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

Lester James Miranda; Takayuki Furuzuki

doi:10.1109/COMPSAC.2018.00074

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

Lester James Miranda, Takayuki Furuzuki

Graduate School of Information, Production and Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 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 language	English
Title of host publication	Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018
Editors	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
Publisher	IEEE Computer Society
Pages	480-485
Number of pages	6
Volume	1
ISBN (Electronic)	9781538626665
DOIs	https://doi.org/10.1109/COMPSAC.2018.00074
Publication status	Published - 2018 Jun 8
Event	42nd IEEE Computer Software and Applications Conference, COMPSAC 2018 - Tokyo, Japan Duration: 2018 Jul 23 → 2018 Jul 27

Other

Other	42nd IEEE Computer Software and Applications Conference, COMPSAC 2018
Country/Territory	Japan
City	Tokyo
Period	18/7/23 → 18/7/27

Keywords

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

ASJC Scopus subject areas

Software
Computer Science Applications

Access to Document

10.1109/COMPSAC.2018.00074

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). Article 8377699 IEEE Computer Society. https://doi.org/10.1109/COMPSAC.2018.00074

A Deep Learning Approach Based on Stacked Denoising Autoencoders for Protein Function Prediction. / Miranda, Lester James; Furuzuki, Takayuki.
Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018. ed. / 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. Vol. 1 IEEE Computer Society, 2018. p. 480-485 8377699.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Miranda, LJ & 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, SI Ahamed, S Reisman, C Demartini & M Nakamura (eds), Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018. vol. 1, 8377699, IEEE Computer Society, pp. 480-485, 42nd IEEE Computer Software and Applications Conference, COMPSAC 2018, Tokyo, Japan, 18/7/23. https://doi.org/10.1109/COMPSAC.2018.00074

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

Miranda, Lester James ; Furuzuki, Takayuki. / A Deep Learning Approach Based on Stacked Denoising Autoencoders for Protein Function Prediction. Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018. editor / 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. Vol. 1 IEEE Computer Society, 2018. pp. 480-485

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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.",

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

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