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Machine Learning Techniques for Protein Secondary Structure Prediction:An Overview and Evaluation

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The prediction of protein secondary structures is not only of great importance for many biological applications but also regarded as an important stepping stone for solving the mystery of how amino acid sequences fold into tertiary structures. Recent research on secondary structure prediction is mainly based on widely known machine learning techniques, such as Artificial Neural Networks and Support Vector Machines. The most significant breakthroughs were the incorporation of new biological information into an efficient prediction model and the development of new models which can efficiently exploit suitable information from its primary sequence. Hence this paper reviews the theoretical and experimental literature of these models with a focus on informational issues involving evolutionary and long-range information of protein sequences. Furthermore, we investigate several key issues in protein data processing which involve dimensionality reduction and encoding schemes.

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Keywords: Amino acids encoding; evolutionary information; long-range dependencies; machine learning; protein secondary structure

Document Type: Research Article

Publication date: May 1, 2008

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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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