Integration of multi-omics data offers several advantages to investigate biological pathways in a more comprehensive manner. It provides better understanding of how a genotype influences a phenotype, as well as the molecular mediators at the transcript and protein levels that regulate the underlying pathway mechanisms. Also, it has the potential to reveal key biological insights into pathways that would otherwise not be made apparent through single-omics studies. On the other-hand, the vast amount of multi-omics data generated has also created new challenges to integrate, visualize and interpret these data in order to study pathways.
Omics technologies have revolutionized our ability to generate global data in a high-throughput manner. Data generation has outpaced our ability to interpret the data and take complete advantage of the information that it provides. Although several methodologies and tools have been developed to deal with high throughput data at either the genome, epigenome, transcriptome or proteome level, they are limited in their ability to integrate these datasets to uncover their relationships for the analysis of biological pathway cross-talk. Thus, novel methodologies and tools that can integrate and leverage the advantages of integrating multi-omics datasets for pathway analysis would be valuable. Moreover, machine learning or deep learning approaches are also required for the model-based inference of multi-omics data for analysis of pathways.
This Research Topic welcomes contributions covering several areas of multi-omics approaches to studying signalling pathways, including:
• novel methodologies for integration, visualization and interpretation of multi-omics data for pathway analysis by incorporating multiple features including gene regulatory networks and protein-protein interactions
• novel methodologies for statistical handling of complexities of fundamental aspects of multi-omics datasets for the analysis of pathways
• applications of machine learning approaches to multi-omics datasets to predict/ infer models of signaling pathways
• new user-friendly tools for pathway analysis, in the form of libraries or application programming interfaces for programming languages, as command line interface and/or as web interface
We welcome a variety of article types including Original Research, Review, Hypothesis and Theory, Methods, Mini Review, Perspective and Systematic Review.
Integration of multi-omics data offers several advantages to investigate biological pathways in a more comprehensive manner. It provides better understanding of how a genotype influences a phenotype, as well as the molecular mediators at the transcript and protein levels that regulate the underlying pathway mechanisms. Also, it has the potential to reveal key biological insights into pathways that would otherwise not be made apparent through single-omics studies. On the other-hand, the vast amount of multi-omics data generated has also created new challenges to integrate, visualize and interpret these data in order to study pathways.
Omics technologies have revolutionized our ability to generate global data in a high-throughput manner. Data generation has outpaced our ability to interpret the data and take complete advantage of the information that it provides. Although several methodologies and tools have been developed to deal with high throughput data at either the genome, epigenome, transcriptome or proteome level, they are limited in their ability to integrate these datasets to uncover their relationships for the analysis of biological pathway cross-talk. Thus, novel methodologies and tools that can integrate and leverage the advantages of integrating multi-omics datasets for pathway analysis would be valuable. Moreover, machine learning or deep learning approaches are also required for the model-based inference of multi-omics data for analysis of pathways.
This Research Topic welcomes contributions covering several areas of multi-omics approaches to studying signalling pathways, including:
• novel methodologies for integration, visualization and interpretation of multi-omics data for pathway analysis by incorporating multiple features including gene regulatory networks and protein-protein interactions
• novel methodologies for statistical handling of complexities of fundamental aspects of multi-omics datasets for the analysis of pathways
• applications of machine learning approaches to multi-omics datasets to predict/ infer models of signaling pathways
• new user-friendly tools for pathway analysis, in the form of libraries or application programming interfaces for programming languages, as command line interface and/or as web interface
We welcome a variety of article types including Original Research, Review, Hypothesis and Theory, Methods, Mini Review, Perspective and Systematic Review.
