Computational Learning Models and Methods Driven by Omics for Biology for “The Fifth China Computer Society Bioinformatics Conference”

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79

authors

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articles

Computational Learning Models and Methods Driven by Omics for Biology for “The Fifth China Computer Society Bioinformatics Conference”

55K

views

79

authors

15

articles

Editors

5

Wang Guohua

Harbin Institute of Technology

Jun Wan

Indiana University

Fa Zhang

Institute of Computing Technology, Chinese Academy of Sciences (CAS)

Chunhou Zheng

Anhui University

Liang Cheng

Harbin Medical University

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About

The Fifth China Computer Society Bioinformatics Conference (CBC 20) will be held in Harbin, October 16-18, 2020. The CBC 20 is supported by China Computer Federation and Northeast Forestry University. The conference will provide a premier forum for the researchers to exchange the latest research advances on bioinformatics, artificial intelligence, medical data analysis and precision medicine, biomedical image analysis, and computational biology.

The central theme of the Research Topic is the statistical and computational methods for integrating omics data. Therefore, we welcome the studies on the following topics, but are not limited to:

• Pipelines for processing multi-omics data;
• Machine learning approaches for analyzing omics data;
• Statistical methods to integrate multi-omics data;
• Identification of novel disease biomarkers;
• Identification of novel drug targets;
• Databases of disease-related novel molecules;
• Web servers for functional analysis of disease-related molecules.

Quantitative analysis needs to be performed on a minimum number of 3 biological replicates in order to enable an assessment of significance and ensure depth of the analysis. This includes quantitative omics studies as well as phenotypic measurements, quantitative assays, and qPCR expression analysis. Studies that do not comply with these replication requirements will not be considered for review.
Studies falling in the categories below will also not be considered for review, unless they are extended to provide meaningful insights into gene/protein function and/or the biology of the subject described. Studies relating to the prediction of clinical outcome require some validation of findings:
• Comparative transcriptomic analyses that only reports a collection of differentially expressed genes, some validated by qPCR under different conditions or treatments;
• Re-analysis of existing genomic, transcriptomic data which attempts to identify a candidate set of diagnostic or prognostic markers for disease.
• Descriptive studies that merely define gene families using basic phylogenetics and assign cursory functional attributions (e.g. expression profiles, hormone or metabolites levels, promoter analysis, informatic parameters).

The Research Topic Computational Learning Models and Methods Driven by Omics for Precision Medicine for the Fourth China Computer Society Bioinformatics Conference is successfully published with 34 articles online.

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Editors

Wang Guohua

Harbin Institute of Technology

Jun Wan

Indiana University

Fa Zhang

Institute of Computing Technology, Chinese Academy of Sciences (CAS)

Chunhou Zheng

Anhui University

Liang Cheng

Harbin Medical University

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Published In

Frontiers in Genetics

Computational Genomics

Frontiers in Plant Science

Computational Genomics

Frontiers in Bioengineering and Biotechnology

Computational Genomics

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