Biological data are multidimensional. To describe a single biological phenomenon, modern researchers combine genomic, transcriptomic, metabolomic, phenomic, environmental, and evolutionary information. Such analyses have increasingly involved a variety of statistical and machine learning techniques, both supervised (e.g., linear regression, random forests) and unsupervised (e.g., clustering, principal component analysis), to extract meaningful patterns out of data and make testable predictions. However, the interconnectedness and interdependence of the multiple types of biological data also lend them to network analyses. Indeed, living systems are networks by their very nature. Yet, advanced network approaches are less common, or perhaps less visible, in computational biology. We wish to highlight the area of computational network algorithms, as well as the biology of networks, and the evolution of biological networks, and stimulate discussion and development of new insights in those fields.
Biological theory and analysis in recent years have experienced a transformation due to the amount and dimensionality of available data. With such large datasets, effectively extracting and summarizing meaningful information is a real challenge. Since any biological system is structurally and functionally a network, computational methods capitalizing on this fact by using graph theory, deep neural network algorithms, graph databasing techniques, or other network-related methodologies, can be particularly successful in biological big data applications. Our goal in this Research Topic is to promote the work on cutting edge methodologies in those areas, and their application to a variety of biological problems, including but not limited to, disease diagnostics, pharmacogenetics, epidemiology, regulatory processes, and evolution at multiple levels of complexity, from intracellular signaling to microbiome or ecosystem.
We welcome all Tier 1 article types supported by Frontiers in Genetics. We are interested in both new methodologies and novel biological findings that utilize network analysis. Methods that utilize biological networks as prior information, focus on reconstruction of networks, or analyze patterns within or between networks are of great interest. Network algorithms for biological applications, software that utilizes network approaches, and hardware or other technology to facilitate network computations, will be appreciated. Studies in the effectiveness, accuracy and computational performance of methods, relevant reviews or comparative analyses, and applications in biological big data, will be of particular interest. We also welcome articles presenting original biological research in this domain, articles on structure, function, and evolution of biological networks, as well as reviews, commentaries, and opinions on those subjects. We especially invite articles that present hybrid approaches combining network algorithms with other methodologies and prior biological information to improve explanatory and predictive power of a biological theory, medical diagnosis, or agricultural productivity. Importantly, we would like this Research Topic to cover a wide variety of model and non-model organisms from all kingdoms of life, as well as biota and ecosystems.
Biological data are multidimensional. To describe a single biological phenomenon, modern researchers combine genomic, transcriptomic, metabolomic, phenomic, environmental, and evolutionary information. Such analyses have increasingly involved a variety of statistical and machine learning techniques, both supervised (e.g., linear regression, random forests) and unsupervised (e.g., clustering, principal component analysis), to extract meaningful patterns out of data and make testable predictions. However, the interconnectedness and interdependence of the multiple types of biological data also lend them to network analyses. Indeed, living systems are networks by their very nature. Yet, advanced network approaches are less common, or perhaps less visible, in computational biology. We wish to highlight the area of computational network algorithms, as well as the biology of networks, and the evolution of biological networks, and stimulate discussion and development of new insights in those fields.
Biological theory and analysis in recent years have experienced a transformation due to the amount and dimensionality of available data. With such large datasets, effectively extracting and summarizing meaningful information is a real challenge. Since any biological system is structurally and functionally a network, computational methods capitalizing on this fact by using graph theory, deep neural network algorithms, graph databasing techniques, or other network-related methodologies, can be particularly successful in biological big data applications. Our goal in this Research Topic is to promote the work on cutting edge methodologies in those areas, and their application to a variety of biological problems, including but not limited to, disease diagnostics, pharmacogenetics, epidemiology, regulatory processes, and evolution at multiple levels of complexity, from intracellular signaling to microbiome or ecosystem.
We welcome all Tier 1 article types supported by Frontiers in Genetics. We are interested in both new methodologies and novel biological findings that utilize network analysis. Methods that utilize biological networks as prior information, focus on reconstruction of networks, or analyze patterns within or between networks are of great interest. Network algorithms for biological applications, software that utilizes network approaches, and hardware or other technology to facilitate network computations, will be appreciated. Studies in the effectiveness, accuracy and computational performance of methods, relevant reviews or comparative analyses, and applications in biological big data, will be of particular interest. We also welcome articles presenting original biological research in this domain, articles on structure, function, and evolution of biological networks, as well as reviews, commentaries, and opinions on those subjects. We especially invite articles that present hybrid approaches combining network algorithms with other methodologies and prior biological information to improve explanatory and predictive power of a biological theory, medical diagnosis, or agricultural productivity. Importantly, we would like this Research Topic to cover a wide variety of model and non-model organisms from all kingdoms of life, as well as biota and ecosystems.
