A deep learning method to more accurately recall known lysine acetylation sites.

Journal: BMC bioinformatics
Published Date: Jan 23, 2019

Abstract

BACKGROUND: Lysine acetylation in protein is one of the most important post-translational modifications (PTMs). It plays an important role in essential biological processes and is related to various diseases. To obtain a comprehensive understanding of regulatory mechanism of lysine acetylation, the key is to identify lysine acetylation sites. Previously, several shallow machine learning algorithms had been applied to predict lysine modification sites in proteins. However, shallow machine learning has some disadvantages. For instance, it is not as effective as deep learning for processing big data.

Authors

  • Meiqi Wu
    Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China.
  • Yingxi Yang
    Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China.
  • Hui Wang
    Department of Vascular Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
  • Yan Xu
    Department of Nephrology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China.

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