Hindawi BioMed Research International Volume 2017, Article ID 5296729, 12 pages https://doi.org/10.1155/2017/5296729 Research Article Novel Approach to Classify Plants Based on Metabolite-Content Similarity Kang Liu, Azian Azamimi Abdullah, Ming Huang, Takaaki Nishioka, Md. Altaf-Ul-Amin, and Shigehiko Kanaya Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan Correspondence should be addressed to Shigehiko Kanaya;
[email protected] Received 6 August 2016; Revised 14 November 2016; Accepted 30 November 2016; Published 9 January 2017 Academic Editor: Yudong Cai Copyright © 2017 Kang Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Secondary metabolites are bioactive substances with diverse chemical structures. Depending on the ecological environment within which they are living, higher plants use different combinations of secondary metabolites for adaptation (e.g., defense against attacks by herbivores or pathogenic microbes). This suggests that the similarity in metabolite content is applicable to assess phylogenic similarity of higher plants. However, such a chemical taxonomic approach has limitations of incomplete metabolomics data. We propose an approach for successfully classifying 216 plants based on their known incomplete metabolite content. Structurally similar metabolites have been clustered using the network clustering algorithm DPClus. Plants have been represented as binary vectors, implying relations with structurally similar metabolite groups, and classified using Ward’s method of hierarchical clustering. Despite incomplete data, the resulting plant clusters are consistent with the known evolutional relations of plants.