In the era of Big Data, leveraging multiple omics data and conducting integrative studies can advance oncogenomics research. Numerous tumor-related biomarkers have been discovered through genome-wide association studies in the last decade. In addition, other omics, including transcriptomics, epigenomics, metabolomics, and proteomics, have updated our understanding of oncology and improved the accuracy of cancer risk prediction.
Although the definition of omics data is well established, deep data-mining of omics data is still insufficient. In addition, new biotechnological (e.g., single-cell sequencing, radionics, electronic medical records) and computational methods (e.g., artificial intelligence, natural language processing) have been developed, both of which require further research. This Research Topic aims to present novel omics-based studies that can aid in the detection of novel biomarkers and the development of cancer prevention, screening, clinical diagnosis, and adjuvant therapy prediction models.
In this Research Topic, we would like to consider submissions of high-quality Original Research and Review articles including (but not limited to) research on the following sub-themes:
-Integration of multi-omics to predict cancer outcomes (risk, survival, et al.)
-Identification of biomarkers from novel omics (e.g., single-cell sequencing, radiomics).
-Novel statistical and computational methods in omics research
-Pan-cancer analysis based on omics
-Novel integration approaches of single omics to multi-level information (e.g. transcriptome-wide association studies)
Disclaimer: The findings based on data integration should be also validated by observational/experimental data. Descriptive studies will 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. Brief Research Reports, Data Reports, Genome Announcements, Systematic Reviews, and Case Reports will not undergo the peer-review process. Studies that fall into the following categories will also not be considered for review:
-1.Comparative transcriptomic analyses that report only a collection of differentially expressed genes, some of which have been validated by qPCR under different conditions or treatments;
-2.Re-analysis of existing genomic and transcriptomic data that attempts to identify a set of diagnostic or prognostic markers for disease.
-3.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).
-4.Studies consisting of publicly available data to develop predictive models.
-5.Descriptive studies that only report sequencing data.
In the era of Big Data, leveraging multiple omics data and conducting integrative studies can advance oncogenomics research. Numerous tumor-related biomarkers have been discovered through genome-wide association studies in the last decade. In addition, other omics, including transcriptomics, epigenomics, metabolomics, and proteomics, have updated our understanding of oncology and improved the accuracy of cancer risk prediction.
Although the definition of omics data is well established, deep data-mining of omics data is still insufficient. In addition, new biotechnological (e.g., single-cell sequencing, radionics, electronic medical records) and computational methods (e.g., artificial intelligence, natural language processing) have been developed, both of which require further research. This Research Topic aims to present novel omics-based studies that can aid in the detection of novel biomarkers and the development of cancer prevention, screening, clinical diagnosis, and adjuvant therapy prediction models.
In this Research Topic, we would like to consider submissions of high-quality Original Research and Review articles including (but not limited to) research on the following sub-themes:
-Integration of multi-omics to predict cancer outcomes (risk, survival, et al.)
-Identification of biomarkers from novel omics (e.g., single-cell sequencing, radiomics).
-Novel statistical and computational methods in omics research
-Pan-cancer analysis based on omics
-Novel integration approaches of single omics to multi-level information (e.g. transcriptome-wide association studies)
Disclaimer: The findings based on data integration should be also validated by observational/experimental data. Descriptive studies will 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. Brief Research Reports, Data Reports, Genome Announcements, Systematic Reviews, and Case Reports will not undergo the peer-review process. Studies that fall into the following categories will also not be considered for review:
-1.Comparative transcriptomic analyses that report only a collection of differentially expressed genes, some of which have been validated by qPCR under different conditions or treatments;
-2.Re-analysis of existing genomic and transcriptomic data that attempts to identify a set of diagnostic or prognostic markers for disease.
-3.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).
-4.Studies consisting of publicly available data to develop predictive models.
-5.Descriptive studies that only report sequencing data.