Glycoinformatics: Data Mining-based Approaches

Abstract

Carbohydrates 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.

Keywords

• Data mining
• Frequent subtrees
• Glycan structures
• Machine learning
• Probabilistic models