Predicting Post-Translational Modifications from Local Sequence Fragments Using Machine Learning Algorithms: Overview and Best Practices.

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ID: 51427
2017
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Ranked #3 of 139 articles by views in methods in molecular biology (clifton, nj)

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Abstract
Here, we present two perspectives on the task of predicting post translational modifications (PTMs) from local sequence fragments using machine learning algorithms. The first is the description of the fundamental steps required to construct a PTM predictor from the very beginning. These steps include data gathering, feature extraction, or machine-learning classifier selection. The second part of our work contains the detailed discussion of more advanced problems which are encountered in PTM prediction task. Probably the most challenging issues which we have covered here are: (1) how to address the training data class imbalance problem (we also present statistics describing the problem); (2) how to properly set up cross-validation folds with an approach which takes into account the homology of protein data records, to address this problem we present our folds-over-clusters algorithm; and (3) how to efficiently reach for new sources of learning features. Presented techniques and notes resulted from intense studies in the field, performed by our and other groups, and can be useful both for researchers beginning in the field of PTM prediction and for those who want to extend the repertoire of their research techniques.
Reference Key
tatjewski2017predictingmethods Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Tatjewski, Marcin;Kierczak, Marcin;Plewczynski, Dariusz;
Journal methods in molecular biology (clifton, nj)
Year 2017
DOI
10.1007/978-1-4939-6406-2_19
URL
Keywords
feature selection phosphorylation feature extraction class imbalance cross-validation

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