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.
| 元の言語 | 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 |
| 出版者 | IEEE Computer Society |
| ページ | 480-485 |
| ページ数 | 6 |
| 巻 | 1 |
| ISBN（電子版） | 9781538626665 |
| DOI | |
| 出版物ステータス | 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 23 → 2018 7 27 |
Other
| Other | 42nd IEEE Computer Software and Applications Conference, COMPSAC 2018 |
|---|---|
| 国 | Japan |
| 市 | Tokyo |
| 期間 | 18/7/23 → 18/7/27 |
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ASJC Scopus subject areas
- Software
- Computer Science Applications
これを引用
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. 版 / 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. 巻 1 IEEE Computer Society, 2018. p. 480-485 8377699.研究成果: Conference contribution
}
TY - GEN
T1 - A Deep Learning Approach Based on Stacked Denoising Autoencoders for Protein Function Prediction
AU - Miranda, Lester James
AU - Furuzuki, Takayuki
PY - 2018/6/8
Y1 - 2018/6/8
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Bioinformatics
KW - Feature extraction
KW - Machine learning
KW - Medical computing
KW - Multi-label classification
UR - http://www.scopus.com/inward/record.url?scp=85055449601&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85055449601&partnerID=8YFLogxK
U2 - 10.1109/COMPSAC.2018.00074
DO - 10.1109/COMPSAC.2018.00074
M3 - Conference contribution
VL - 1
SP - 480
EP - 485
BT - Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018
A2 - Lung, Chung-Horng
A2 - Conte, Thomas
A2 - Liu, Ling
A2 - Akiyama, Toyokazu
A2 - Hasan, Kamrul
A2 - Tovar, Edmundo
A2 - Takakura, Hiroki
A2 - Claycomb, William
A2 - Cimato, Stelvio
A2 - Yang, Ji-Jiang
A2 - Zhang, Zhiyong
A2 - Ahamed, Sheikh Iqbal
A2 - Reisman, Sorel
A2 - Demartini, Claudio
A2 - Nakamura, Motonori
PB - IEEE Computer Society
ER -