Mini-review : Recent advances in post-translational modification site prediction based on deep learning

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

15 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)3522-3532
Journal / PublicationComputational and Structural Biotechnology Journal
Volume20
Online published30 Jun 2022
Publication statusPublished - 2022

Link(s)

Abstract

Post-translational modifications (PTMs) are closely linked to numerous diseases, playing a significant role in regulating protein structures, activities, and functions. Therefore, the identification of PTMs is crucial for understanding the mechanisms of cell biology and diseases therapy. Compared to traditional machine learning methods, the deep learning approaches for PTM prediction provide accurate and rapid screening, guiding the downstream wet experiments to leverage the screened information for focused studies. In this paper, we reviewed the recent works in deep learning to identify phosphorylation, acetylation, ubiquitination, and other PTM types. In addition, we summarized PTM databases and discussed future directions with critical insights.

Research Area(s)

  • Deep learning, Machine learning, Mass spectrometry, Post-translational modification, Prediction

Citation Format(s)

Mini-review: Recent advances in post-translational modification site prediction based on deep learning. / Meng, Lingkuan; Chan, Wai-Sum; Huang, Lei et al.
In: Computational and Structural Biotechnology Journal, Vol. 20, 2022, p. 3522-3532.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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