Editorial: Predicting High-Risk Individuals for Common Diseases Using Multi-Omics and Epidemiological Data

Front Genet. 2021 Aug 18:12:737598. doi: 10.3389/fgene.2021.737598. eCollection 2021.

Debajyoti Chowdhury^{1

2}, Xin Zhou³, Bailiang Li⁴, Yuanwei Zhang⁵, William K Cheung⁶, Aiping Lu^{1

2}, Lu Zhang^{1

6}

¹ Computational Medicine Lab, Hong Kong Baptist University, Kowloon Tong, Hong Kong.
² School of Chinese Medicine, Institute of Integrated Bioinformedicine and Translational Sciences, Hong Kong Baptist University, Kowloon Tong, Hong Kong.
³ Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States.
⁴ Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, United States.
⁵ The Chinese Academy of Sciences Key Laboratory of Innate Immunity and Chronic Diseases, Hefei National Laboratory for Physical Sciences at the Microscale, Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Collaborative Innovation Center of Genetics and Development, School of Life Sciences, The First Affiliated Hospital of University of Science and Technology of China, Hefei, China.
⁶ Department of Computer Science, Faculty of Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong.

No abstract available

Keywords: complex diseases; data integration analysis; disease prediction; machine learning; multi-omics analyses.

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