High-throughput technologies are generating large quantities of data. These data provide a snapshot of the molecular environment and can include transcriptomic, epigenomic, and genomic information. Network approaches are a powerful way to model the biological processes measured by these data. Over the past decade, network inference and reconstruction algorithms have been developed and applied in a variety of organisms and tissues to model interactions between genes and gene products in the cell. However, our current view of genomic networks is still limited. There is an active field of computational and network biology research that aims to improve network modeling, to expand networks by integrating various types of `omics data, to model and analyze genomic networks between different conditions, and to model and analyze networks for individual samples, cell types, or cells.
Network approaches hold great promise in facilitating our understanding of biological processes, as well as their relationship to health and disease. However, there are many challenges that impede translating `omics data into meaningful networks, and in leveraging networks effectively to gain new insights into biological mechanisms and/or impact patient outcomes. Networks derived from `omics data are often very large and therefore difficult to model, analyze, and interpret. New methods are needed to infer networks for use in biology and medicine. Importantly, to support the translational potential of network analysis is critical to show the merits of network approaches beyond other analysis techniques. Our goal in this Research Topic is to provide a collection of articles that (1) examine or propose innovative and novel methods for biological network inference and analysis, or (2) illustrate the impact of network approaches through specific applications in biology and medicine. Articles that combine both methods and application are especially encouraged.
The aim of the current Research Topic is to cover promising, recent, and novel research trends in the field of Genomic Networks. Areas to be covered in this Research Topic may include, but are not limited to:
• Development of network inference methods
• Network approaches for analyzing and interpreting large-scale biological datasets
• Analysis of large-scale biological networks
• Heterogeneous data integration using networks
• Integration of biological networks
• Applications in Network Medicine
• Gene regulatory networks underlying plasticity and evolution
We strongly encourage the submission of Original Research and Methods articles, but also welcome submissions in the format of Data Report, Perspective, Systematic Review, Review, and Mini Review.
**Upon submission, up until the deadline, manuscripts will go through editorial checks and will be sent out for review immediately as long as they are in scope for the Research Topic and Journal**
High-throughput technologies are generating large quantities of data. These data provide a snapshot of the molecular environment and can include transcriptomic, epigenomic, and genomic information. Network approaches are a powerful way to model the biological processes measured by these data. Over the past decade, network inference and reconstruction algorithms have been developed and applied in a variety of organisms and tissues to model interactions between genes and gene products in the cell. However, our current view of genomic networks is still limited. There is an active field of computational and network biology research that aims to improve network modeling, to expand networks by integrating various types of `omics data, to model and analyze genomic networks between different conditions, and to model and analyze networks for individual samples, cell types, or cells.
Network approaches hold great promise in facilitating our understanding of biological processes, as well as their relationship to health and disease. However, there are many challenges that impede translating `omics data into meaningful networks, and in leveraging networks effectively to gain new insights into biological mechanisms and/or impact patient outcomes. Networks derived from `omics data are often very large and therefore difficult to model, analyze, and interpret. New methods are needed to infer networks for use in biology and medicine. Importantly, to support the translational potential of network analysis is critical to show the merits of network approaches beyond other analysis techniques. Our goal in this Research Topic is to provide a collection of articles that (1) examine or propose innovative and novel methods for biological network inference and analysis, or (2) illustrate the impact of network approaches through specific applications in biology and medicine. Articles that combine both methods and application are especially encouraged.
The aim of the current Research Topic is to cover promising, recent, and novel research trends in the field of Genomic Networks. Areas to be covered in this Research Topic may include, but are not limited to:
• Development of network inference methods
• Network approaches for analyzing and interpreting large-scale biological datasets
• Analysis of large-scale biological networks
• Heterogeneous data integration using networks
• Integration of biological networks
• Applications in Network Medicine
• Gene regulatory networks underlying plasticity and evolution
We strongly encourage the submission of Original Research and Methods articles, but also welcome submissions in the format of Data Report, Perspective, Systematic Review, Review, and Mini Review.
**Upon submission, up until the deadline, manuscripts will go through editorial checks and will be sent out for review immediately as long as they are in scope for the Research Topic and Journal**
