Abstract
One of the major challenges in the post-genomic era is to accurately model the interactions taking place in most cellular processes. Detailed characterization of such interactions is critical for understanding the principles of living cell molecular machinery on the system biology level. This book chapter contains a review of the multiscale protein biological function prediction algorithms that are founded on protein sequence analysis, three-dimensional structure comparison, biological function annotation, and finally molecular interactions. We include diverse computational methods used to predict the biological function for a given biomolecule using multiscale features, and more generally to model a meta-learning prediction system to analyze the impact of micro-dynamics on global behavior for selected biological systems, with important roles in chemistry, biology, and medicine.
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Plewczynski, D., Basu, S. (2014). A Meta-learning Approach for Protein Function Prediction. In: Saha, P., Maulik, U., Basu, S. (eds) Advanced Computational Approaches to Biomedical Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41539-5_5
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