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 to some extent determines the fate of identified biomarkers or molecular signatures. From these considerations, the development of appropriate normalization methods or normalization-free algorithms may promote biomarker identification and enhance disease diagnostics.
We welcome the submission of Original Research papers, Methods papers, as well as Review articles on algorithms and applications of omics data analysis especially in the context of biomarker identification and data normalization. Please note that studies relating to the prediction of clinical outcomes require some validation of findings. Topics of interest include but are not limited to:
• Machine learning and deep learning algorithms for predictive analyses;
• Application of omics data in disease prognostics, diagnostics, and treatment;
• Algorithms for the integration of multi-omics data, including genomics, transcriptomics, proteomics, epigenomics, metagenomics, metabolomics, etc.;
• Algorithms for bulk or single-cell data analysis including quality control, normalization, clustering, differential analysis, etc.;
• Algorithms for characterization and visualization of omics datasets;
• Software tools and databases.
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 to some extent determines the fate of identified biomarkers or molecular signatures. From these considerations, the development of appropriate normalization methods or normalization-free algorithms may promote biomarker identification and enhance disease diagnostics.
We welcome the submission of Original Research papers, Methods papers, as well as Review articles on algorithms and applications of omics data analysis especially in the context of biomarker identification and data normalization. Please note that studies relating to the prediction of clinical outcomes require some validation of findings. Topics of interest include but are not limited to:
• Machine learning and deep learning algorithms for predictive analyses;
• Application of omics data in disease prognostics, diagnostics, and treatment;
• Algorithms for the integration of multi-omics data, including genomics, transcriptomics, proteomics, epigenomics, metagenomics, metabolomics, etc.;
• Algorithms for bulk or single-cell data analysis including quality control, normalization, clustering, differential analysis, etc.;
• Algorithms for characterization and visualization of omics datasets;
• Software tools and databases.
