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Manuscript Submission Deadline 31 January 2023

Comprehensive understanding of complex human disorders requires integrative analysis of multiple levels of data from genome, transcriptome, epigenome, proteome, and metabolome. Recently next-generation sequencing technologies have made significant progress, enabling researchers to perform genome-wide measurements. The integration and analysis of such datasets—mRNA expression, DNA methylation, copy-number variations, single-nucleotide polymorphism — strengthens our knowledge of disease etiology. Complex human diseases evolved due to perturbations at multiple molecular levels, and multiscale characterization of such complex diseases allows us to capture additional sources of variability. This has led to the development of novel disease prediction, early detection, and prevention strategies as well as improving therapeutic applications and our understanding of drug response.

In the cellular system, genes/proteins form a community-like structure to accomplish the biological functions; their interactions result in forming a molecular or biological network. Perturbation in genes/proteins results in altering molecular networks that lead to diseased states. Therefore, studying molecular networks' architectural details can reveal the disease mechanism and biomarker discovery. Furthermore, the biological process inside a cell is regulated at multiple levels by tightly controlled transcriptional, post-transcriptional, and post-translational molecular networks. Additionally, the interconnected nature of the biomolecules transmits the molecular perturbations throughout the cellular network resulting in increased complexity, which poses significant challenges to comprehending mechanism of disease formation. Hence the development of advanced network-based toolsets to integrate and construct the network from multi-scale omic data and their analysis can aid in deciphering complex mechanisms of human disorders.

This topic focuses on recent advances, current challenges, latest discoveries, and future perspectives in the field of multi-scale omics data integration and network biology approaches.

The scope of this issue broadly covers, but is not limited to:
• Development of new tools/method/framework/algorithm for multi-scale omics data integration for deciphering complex disorders/disease
• Disorder/Disease subtyping, grading, and classification using omics/multi-omics profiles
• Role of multi-scale omics data integration with network biology approaches in designing personalized therapy
• Deep learning/machine learning for the analysis of complex disorders/diseases using multi-scale omics data integration
• Genetic or epigenetic marker discovery, clustering, community detection, differential expression/methylation analysis.
• Construction of molecular network from multi-scale omics data for biomarker and drug discovery
• Biological network for disease modules identification and disease network construction.
• Construction and analysis of Protein-protein interaction networks, co-expression networks, gene/transcriptional regulatory networks, metabolic networks for biomarker discovery.
• Artificial Intelligence in Network biology.

Keywords: Systems Biology, Network Biology, Muti-scale, Omics, NGS, Disease, Disorder, AI, Machine Learning


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.

Comprehensive understanding of complex human disorders requires integrative analysis of multiple levels of data from genome, transcriptome, epigenome, proteome, and metabolome. Recently next-generation sequencing technologies have made significant progress, enabling researchers to perform genome-wide measurements. The integration and analysis of such datasets—mRNA expression, DNA methylation, copy-number variations, single-nucleotide polymorphism — strengthens our knowledge of disease etiology. Complex human diseases evolved due to perturbations at multiple molecular levels, and multiscale characterization of such complex diseases allows us to capture additional sources of variability. This has led to the development of novel disease prediction, early detection, and prevention strategies as well as improving therapeutic applications and our understanding of drug response.

In the cellular system, genes/proteins form a community-like structure to accomplish the biological functions; their interactions result in forming a molecular or biological network. Perturbation in genes/proteins results in altering molecular networks that lead to diseased states. Therefore, studying molecular networks' architectural details can reveal the disease mechanism and biomarker discovery. Furthermore, the biological process inside a cell is regulated at multiple levels by tightly controlled transcriptional, post-transcriptional, and post-translational molecular networks. Additionally, the interconnected nature of the biomolecules transmits the molecular perturbations throughout the cellular network resulting in increased complexity, which poses significant challenges to comprehending mechanism of disease formation. Hence the development of advanced network-based toolsets to integrate and construct the network from multi-scale omic data and their analysis can aid in deciphering complex mechanisms of human disorders.

This topic focuses on recent advances, current challenges, latest discoveries, and future perspectives in the field of multi-scale omics data integration and network biology approaches.

The scope of this issue broadly covers, but is not limited to:
• Development of new tools/method/framework/algorithm for multi-scale omics data integration for deciphering complex disorders/disease
• Disorder/Disease subtyping, grading, and classification using omics/multi-omics profiles
• Role of multi-scale omics data integration with network biology approaches in designing personalized therapy
• Deep learning/machine learning for the analysis of complex disorders/diseases using multi-scale omics data integration
• Genetic or epigenetic marker discovery, clustering, community detection, differential expression/methylation analysis.
• Construction of molecular network from multi-scale omics data for biomarker and drug discovery
• Biological network for disease modules identification and disease network construction.
• Construction and analysis of Protein-protein interaction networks, co-expression networks, gene/transcriptional regulatory networks, metabolic networks for biomarker discovery.
• Artificial Intelligence in Network biology.

Keywords: Systems Biology, Network Biology, Muti-scale, Omics, NGS, Disease, Disorder, AI, Machine Learning


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.

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