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 |
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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 |
巻 | 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 23 → 2018 7 27 |
Other
Other | 42nd IEEE Computer Software and Applications Conference, COMPSAC 2018 |
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Country | Japan |
City | Tokyo |
Period | 18/7/23 → 18/7/27 |
ASJC Scopus subject areas
- Software
- Computer Science Applications