Estimation of relationships between chemical substructures and antibiotic resistance-related gene expression in bacteria: Adapting a canonical correlation analysis for small sample data of gathered features using consensus clustering

DOI Web Site 8 References Open Access
  • Esaki Tsuyoshi
    The Center for Data Science Education and Research, Shiga University, 1-1-1 Banba, Hikone, Shiga 522-8522, Japan
  • Horinouchi Takaaki
    Center for Biosystems Dynamics Research, RIKEN, 6-2-3 Furuedai, Suita, Osaka 565-0874, Japan
  • Natsume-Kitatani Yayoi
    The Center for Data Science Education and Research, Shiga University, 1-1-1 Banba, Hikone, Shiga 522-8522, Japan Center of Drug Design Research, National Institutes of Biomedical Innovation, Health and Nutrition, 7-8-6 Saito
  • Nojima Yosui
    Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation,
  • Sakane Iwao
    Central Research Institute, ITO EN Ltd., 21 Megami, Makinohara, Shizuoka 421-0516, Japan
  • Matsui Hidetoshi
    The Center for Data Science Education and Research, Shiga University, 1-1-1 Banba, Hikone, Shiga 522-8522, Japan Faculty of Data Science, Shiga University, 1-1-1 Banba, Hikone, Shiga 522-8522, Japan

Abstract

<p>The emergence of antibiotic-resistant bacteria is a serious public health concern. Understanding the relationships between antibiotic compounds and phenotypic changes related to the acquisition of resistance is important to estimate the effective characteristics of drug seeds. It is important to analyze the relationships between phenotypic changes and compound structures; hence, we performed a canonical correlation analysis (CCA) for high dimensional phenotypic and compound structure datasets. For the CCA, the required sample number must be larger than the feature number; however, collecting a large amount of data can sometimes be difficult. Thus, we combined consensus clustering to gather and reduce features. The CCA was performed using the clustered features, and it revealed relationships between the features of chemical substructures and the expression level of genes related to several types of antibiotic resistance.</p>

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