A hybrid machine learning model with attention mechanism and multidimensional multivariate feature coding for essential gene prediction.

Journal: BMC biology
PMID: 40275343

Abstract

BACKGROUND: Essential genes are crucial for the development, inheritance, and survival of species. The exploration of these genes can unravel the complex mechanisms and fundamental life processes and identify potential therapeutic targets for various diseases. Therefore, the identification of essential genes is significant. Machine learning has become the mainstream approach for essential gene prediction. However, some key challenges in machine learning need to be addressed, such as the extraction of genetic features, the impact of imbalanced data, and the cross-species generalization ability.

Authors

  • Wu Yan
    Guangdong Key Laboratory of Modern Control Technology, Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou, China.
  • Fu Yu
    College of Physics and Electronic Information, Gannan Normal University, Ganzhou, 341000, Jiangxi, China.
  • Li Tan
    Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, Shantou, Guangdong, China.
  • Li Mengshan
    College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China. msli@gnnu.edu.cn.
  • Xie Xiaojun
    College of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, Jiangxi, China.
  • Zhou Weihong
    School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, 212018, China.
  • Sheng Sheng
    School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, 212018, China.
  • Wang Jun
    Department of Thoracic Surgery, The Second Hospital Affiliated to Harbin Medical University, #148 Baojian Road, Harbin, 150001, China.
  • Wu Fu-An
    School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, 212018, China. fuan_w@just.edu.cn.

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