This Research Topic is part of a series with, "Bioinformatics Analysis of Omics Data for Biomarker Identification in Clinical Research - Volume I" (https://www.frontiersin.org/research-topics/13816/bioinformatics-analysis-of-omics-data-for-biomarker-identification-in-clinical-research)
The advances and the decreasing cost of omics data enable profiling of disease molecular features at different levels, including bulk tissues, animal models, and single cells. Large volumes of omics data enhance the ability to search for information for preclinical study and provide the opportunity to leverage them to understand disease mechanisms, identify molecular targets for therapy, and detect biomarkers of treatment response.
Identification of stable, predictive, and interpretable biomarkers is a significant step towards personalized medicine and therapy. Omics data from genomics, transcriptomics, proteomics, epigenomics, metagenomics, and metabolomics help to determine biomarkers for prognostic and diagnostic applications. Preprocessing of omics data is of vital importance as it aims to eliminate systematic experimental bias and technical variation while preserving biological variation. Dozens of normalization methods for correcting experimental variation and bias in omics data have been developed during the last two decades, while only a few consider the skewness between different sample states, such as the extensive over-repression of genes in cancers. The choice of normalization methods determines the fate of identified biomarkers or molecular signatures. From these considerations, the development of appropriate normalization methods or preprocessing strategies may promote biomarker identification and facilitate clinical decision-making.
We welcome the submission of Original Research, Methods, as well as Review articles on algorithms and applications of omics data analysis especially in the context of biomarker identification and data preprocessing. Please note that studies relating to the prediction of clinical outcomes require validation of findings, such as molecular validation of biomarkers or mechanistic insight to biomarkers, not simply confirmation in a different computational dataset. Topics of interest include but are not limited to:
• Machine learning and deep learning algorithms for biomarker identification;
• Application of omics data in disease prognostics, diagnostics, and treatment;
• Algorithms for the large-scale integration of omics data;
• Algorithms for bulk and/or single-cell data analysis including quality control, normalization, clustering, differential analysis, information transfer, etc.;
• Algorithms and tools for characterization and visualization of omics datasets.
This Research Topic is part of a series with, "Bioinformatics Analysis of Omics Data for Biomarker Identification in Clinical Research - Volume I" (https://www.frontiersin.org/research-topics/13816/bioinformatics-analysis-of-omics-data-for-biomarker-identification-in-clinical-research)
The advances and the decreasing cost of omics data enable profiling of disease molecular features at different levels, including bulk tissues, animal models, and single cells. Large volumes of omics data enhance the ability to search for information for preclinical study and provide the opportunity to leverage them to understand disease mechanisms, identify molecular targets for therapy, and detect biomarkers of treatment response.
Identification of stable, predictive, and interpretable biomarkers is a significant step towards personalized medicine and therapy. Omics data from genomics, transcriptomics, proteomics, epigenomics, metagenomics, and metabolomics help to determine biomarkers for prognostic and diagnostic applications. Preprocessing of omics data is of vital importance as it aims to eliminate systematic experimental bias and technical variation while preserving biological variation. Dozens of normalization methods for correcting experimental variation and bias in omics data have been developed during the last two decades, while only a few consider the skewness between different sample states, such as the extensive over-repression of genes in cancers. The choice of normalization methods determines the fate of identified biomarkers or molecular signatures. From these considerations, the development of appropriate normalization methods or preprocessing strategies may promote biomarker identification and facilitate clinical decision-making.
We welcome the submission of Original Research, Methods, as well as Review articles on algorithms and applications of omics data analysis especially in the context of biomarker identification and data preprocessing. Please note that studies relating to the prediction of clinical outcomes require validation of findings, such as molecular validation of biomarkers or mechanistic insight to biomarkers, not simply confirmation in a different computational dataset. Topics of interest include but are not limited to:
• Machine learning and deep learning algorithms for biomarker identification;
• Application of omics data in disease prognostics, diagnostics, and treatment;
• Algorithms for the large-scale integration of omics data;
• Algorithms for bulk and/or single-cell data analysis including quality control, normalization, clustering, differential analysis, information transfer, etc.;
• Algorithms and tools for characterization and visualization of omics datasets.
