Background: Proteins through post translational modifications perform their biological
process and cellular functions. Post-translational modifications play important roles in various biological
process and cell functions. Identifying the PTMs sites in proteins is very significant to basic
research and drug design. Experimental technique to identify post translational modifications is
laborious. Computational identification of post translational modifications is a complementary way
for its convenience.
Conclusion: This review gives the processing to predict post-translational modification sites in
proteins including feature construction, algorithms, evaluation measurement, and online webserver.
There are two types of post translational modification in proteins. In the prediction
of single PTM sites, we transformed it into binary classification learning. While in the prediction
of crosstalk PTM sites, we transformed it into multi-label learning. This review summarized the
steps on the two issues.
Keywords: Post translational modifications, machine learning, web-server, feature construction, algorithm, binary classification
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