PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies.

Journal: Genome medicine
Published Date: Apr 16, 2024

Abstract

Despite the abundance of genotype-phenotype association studies, the resulting association outcomes often lack robustness and interpretations. To address these challenges, we introduce PheSeq, a Bayesian deep learning model that enhances and interprets association studies through the integration and perception of phenotype descriptions. By implementing the PheSeq model in three case studies on Alzheimer's disease, breast cancer, and lung cancer, we identify 1024 priority genes for Alzheimer's disease and 818 and 566 genes for breast cancer and lung cancer, respectively. Benefiting from data fusion, these findings represent moderate positive rates, high recall rates, and interpretation in gene-disease association studies.

Authors

  • Xinzhi Yao
    College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China.
  • Sizhuo Ouyang
    College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China.
  • Yulong Lian
    College of Science, Huazhong Agricultural University, Wuhan, China.
  • Qianqian Peng
    College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China.
  • Xionghui Zhou
    Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, People's Republic of China.
  • Feier Huang
    College of Life Science and Technology, Huazhong Agricultural University, Wuhan, China.
  • Xuehai Hu
    College of Informatics, Agricultural Bioinformatics Key Laboratory of Hubei Province, Huazhong Agricultural University, Wuhan 430070, P.R. China.
  • Feng Shi
    Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd. Shanghai, China.
  • Jingbo Xia
    Tan KahKee College, Xiamen University, Xiamen, Fujian, China.

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