Open AccessGlycoinformatics: Data Mining-based Approaches
Author(s) -
Hiroshi Mamitsuka
Publication year - 2011
Publication title -
chimia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.387
H-Index - 55
eISSN - 2673-2424
pISSN - 0009-4293
DOI - 10.2533/chimia.2011.10
Subject(s) - viewpoints , cluster analysis , glycan , variety (cybernetics) , computer science , data mining , term (time) , data science , machine learning , artificial intelligence , chemistry , art , biochemistry , physics , quantum mechanics , glycoprotein , visual arts
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.