Glycoinformatics: Data Mining-based Approaches
Keywords:Data mining, Frequent subtrees, Glycan structures, Machine learning, Probabilistic models
AbstractCarbohydrates or glycans are major cellular macromolecules, working for a variety of vital biological functions. Due to long-term efforts by experimentalists, the current number of structurally different, determined carbohydrates has exceeded 10,000 or more. As a result data mining-based approaches for glycans (or trees in a computer science sense) have attracted attention and have been developed over the last five years, presenting new techniques even from computer science viewpoints. This review summarizes cutting-edge techniques for glycans in each of the three categories of data mining: classification, clustering and frequent pattern mining, and shows results obtained by applying these techniques to real sets of glycan structures.
How to Cite
H. Mamitsuka, Chimia 2011, 65, 10, DOI: 10.2533/chimia.2011.10.
Copyright (c) 2011 Swiss Chemical Society
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