Analysis of PTMs presents a number of challenges to protein and proteomics researchers, and efficient and sensitive methods for detection of PTMs are required. Traditionally, PTMs have been identified using mass spectrometry (MS), it has proven to be extremely useful in PTM discovery. However, the utilization of MS in PTMs analysis is costly in terms of material, time and human resource.
Computational approaches have been widely used in PTMs analysis. The advancement of computational apparatus has brought the computational power became more available. We applied various machine learning methods to PTMs data, to help advance protein and proteomic research.
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