About this Research Topic
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.
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.
Keywords: Bioinformatics, Genomics, Transcriptomics, Omics, Statistical, Data Integration, Artificial Intelligence, Genetics, System Biology
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
