StressGenePred: a twin prediction model architecture for classifying the stress types of samples and discovering stress-related genes in arabidopsis.

Journal: BMC genomics
Published Date: Dec 20, 2019

Abstract

BACKGROUND: Recently, a number of studies have been conducted to investigate how plants respond to stress at the cellular molecular level by measuring gene expression profiles over time. As a result, a set of time-series gene expression data for the stress response are available in databases. With the data, an integrated analysis of multiple stresses is possible, which identifies stress-responsive genes with higher specificity because considering multiple stress can capture the effect of interference between stresses. To analyze such data, a machine learning model needs to be built.

Authors

  • Dongwon Kang
    Department of Biomedical Engineering, Yonsei University College of Health Science, Wonju, Korea.
  • Hongryul Ahn
    Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.
  • Sangseon Lee
    Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.
  • Chai-Jin Lee
    Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
  • Jihye Hur
    Department of Crop Science, Konkuk University, Seoul, Republic of Korea.
  • Woosuk Jung
    Department of Crop Science, Konkuk University, Seoul, Republic of Korea. jungw@konkuk.ac.kr.
  • Sun Kim
    National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, 20894, MD, USA. sun.kim@nih.gov.

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