Computational Learning Models and Methods Driven by Omics for Precision Medicine

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Editorial

23 December 2020

Editorial: Computational Learning Models and Methods Driven by Omics for Precision Medicine

Lei Zhu

,

Hongmin Cai

,

Fa Zhang

,

Quan Zou

, 1 more and

Huiru Zheng

1,725 views

1 citations

Editors

5

South China University of Technology

School of Computing, Engineering and Intelligent Systems, Faculty of Computing, Engineering and the Built Environment, Ulster University

Beijing Institute of Technology

University of Electronic Science and Technology of China

Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (CAS)

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Recent technological developments have helped in facilitating the production of multiple genome-wide high throughput biological data types. These include omics data, which characterize genomics, transcriptomics, proteomics, and other micro-scale activities. Current imaging technologies have also produced macro-scale phenotype measurements. Analysis of such verified scale datasets has been proven to be invaluable for basic biological research and precision medicine. Therefore, the exciting advances in bioinformatics and computational biology call for novel methods to cater to the unprecedented needs, rising in this area.

This Research Topic welcomes articles on computational biology and bioinformatics to be presented at the Fourth China Computer Society Bioinformatics Conference (http://www2.scut.edu.cn/CBC2019/) in Guangzhou, which was launched by China Computer Federation (https://www.ccf.org.cn/). The CBC has been successfully held for three years and is one of the most well-known conferences in computational biology and bioinformatics in China. This year's conference will include several symposia on bioinformatics, medical data analysis and precision medicine, biomedical image analysis, and computational biology.

The central theme of this Research Topic is the computational methodologies and algorithms driven by omics data for recovering the dynamic and complex mechanism for understanding precision therapy. The current collection will include post-conference manuscripts, presenting the highlights from the Conference CCF-CBC 2019.

Recent technological developments have helped in facilitating the production of multiple genome-wide high throughput biological data types. These include omics data, which characterize genomics, transcriptomics, proteomics, and other micro-scale activities. Current imaging technologies have also produced macro-scale phenotype measurements. Analysis of such verified scale datasets has been proven to be invaluable for basic biological research and precision medicine. Therefore, the exciting advances in bioinformatics and computational biology call for novel methods to cater to the unprecedented needs, rising in this area.

This Research Topic welcomes articles on computational biology and bioinformatics to be presented at the Fourth China Computer Society Bioinformatics Conference (http://www2.scut.edu.cn/CBC2019/) in Guangzhou, which was launched by China Computer Federation (https://www.ccf.org.cn/). The CBC has been successfully held for three years and is one of the most well-known conferences in computational biology and bioinformatics in China. This year's conference will include several symposia on bioinformatics, medical data analysis and precision medicine, biomedical image analysis, and computational biology.

The central theme of this Research Topic is the computational methodologies and algorithms driven by omics data for recovering the dynamic and complex mechanism for understanding precision therapy. The current collection will include post-conference manuscripts, presenting the highlights from the Conference CCF-CBC 2019.

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Editors

South China University of Technology

School of Computing, Engineering and Intelligent Systems, Faculty of Computing, Engineering and the Built Environment, Ulster University

Beijing Institute of Technology

University of Electronic Science and Technology of China

Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (CAS)

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Frontiers in Genetics

Computational Genomics

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Frontiers in Plant Science

Computational Genomics

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Frontiers in Bioengineering and Biotechnology

Computational Genomics

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