Using Deep Learning with Position Specific Scoring Matrices to Identify Efflux Proteins in Membrane and Transport Proteins

Semmy Wellem Taju, Nguyen Quoc Khanh Le, Yu Yen Ou

研究成果: 書貢獻/報告類型會議貢獻

3 引文 斯高帕斯(Scopus)

摘要

In several years, deep learning is a new area of machine learning field, which is the motivation of developing machine learning near to artificial intelligent. The neural networks belongs to deep learning are progressively important ideas in a variety of fields with great performance. Accordingly, utilization of deep learning in bioinformatics to enhance performance is very important. Convolutional neural networks is a network of deep learning which is claimed to be the best model to solve the problem of object recognition and detection utilizing GPU computing. In this study, we try to use CNN to identify efflux proteins in membrane and transport proteins, which is a famous problem in bioinformatics field. We construct the CNN from PSSM profiles with CUDA and Keras package based on Theano backend. Finally this approach achieved a significant improvement after we compare with the previous paper on efflux proteins. The proposed method can serve as an effective tool for identifying efflux proteins and can help biologists understand the functions of the efflux proteins. Moreover this study provides a basis for further research that can enrich a field of applying deep learning in bioinformatics.
原文英語
主出版物標題Proceedings - 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, BIBE 2016
發行者Institute of Electrical and Electronics Engineers Inc.
頁面101-108
頁數8
ISBN(電子)9781509038336
DOIs
出版狀態已發佈 - 十二月 16 2016
對外發佈
事件16th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2016 - Taichung, 臺灣
持續時間: 十月 31 2016十一月 2 2016

出版系列

名字Proceedings - 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, BIBE 2016

會議

會議16th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2016
國家/地區臺灣
城市Taichung
期間10/31/1611/2/16

ASJC Scopus subject areas

  • 生物技術
  • 遺傳學
  • 生物工程
  • 生物醫學工程
  • 健康資訊學

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