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Machine Learning Sequence Classification Techniques: Application to Cysteine Protease Cleavage Prediction

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Sequence classification is one of the most fundamental machine learning tasks in computational biology nowadays. With the wide availability of large corpora of annotated sequences, the use of supervised learning techniques can greatly speed up the process of identifying new sequences sharing certain function or properties. Many methods have been proposed over the years and we hope to provide an introduction to some of the more prominent ones by focussing on protease cleavage prediction: a typical representative of this class of problem. The variety of proteolytic action modes between cysteine-proteases covers a broad range of complexity level and feature specificity, illustrating the strengths and limitations of the different machine learning techniques used on them.

This review briefly introduces the particulars of predicting cleavage by calpains and caspases. We then offer some general practical considerations on treating sequences for use with machine learning algorithms, before covering specific methods. The methods presented range from basic position-based statistical models to more technically advanced methods such as Markov models or kernel-based algorithms, as well as methods with more restricted goals such as decision trees. With each family of algorithms, examples of implementations are introduced and their performances compared, along with particular strengths and weaknesses.

With this review, we aim to provide useful elements of decision toward choosing an existing method or developing a new one, based on the complexity and specific needs of a given sequence classification problem.
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Keywords: Calpain; Markov Models; Vector Encoding; caspase; cleavage prediction; cysteine proteases; machine learning; protease; proteolysis; sequence classification

Document Type: Research Article

Publication date: 01 December 2012

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  • Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth reviews written by leaders in the field, covering a wide range of the integration of biology with computer and information science.

    The journal focuses on reviews on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.

    Current Bioinformatics is an essential journal for all academic and industrial researchers who want expert knowledge on all major advances in bioinformatics.
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