SSL-VQ: vector-quantized variational autoencoders for semi-supervised prediction of therapeutic targets across diverse diseases.

Journal: Bioinformatics (Oxford, England)
PMID: 39880378

Abstract

MOTIVATION: Identifying effective therapeutic targets poses a challenge in drug discovery, especially for uncharacterized diseases without known therapeutic targets (e.g. rare diseases, intractable diseases).

Authors

  • Satoko Namba
    Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Kawazu, Iizuka, Fukuoka, 820-8502, Japan.
  • Chen Li
    School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
  • Noriko Yuyama Otani
    Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Kawazu, Iizuka, Fukuoka, 820-8502, Japan.
  • Yoshihiro Yamanishi
    Division of System Cohort, Multi-Scale Research Center for Medical Science, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka, 812-8582, Japan. yamanishi@bioreg.kyushu-u.ac.jp.

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