The role of mathematical modelling has a long history in physiology as a valuable tool to understand the complexity of the biological function. Typically, this complexity is shaped by the existence of nonlinear relationships, and stochastic and time-varying dynamics. Today, the availability of extensive collections of biomedical and clinical data, together with molecular data thanks to the widespread use of omics technologies, allows researchers to unravel part of this complexity. It is essential to use proper statistical and computational methods to derive knowledge and robust results from these datasets, even generating new estimated values in this context. Disciplines such as biostatistics, bioinformatics or systems biology, sometimes in conjunction with biomechanics, lead the success of this data revolution in the analysis of physiological processes.
This Research Topic is dedicated to cover the development of advanced data analysis methodologies and its application to the analysis of molecular, image, text or clinical data in physiology. We welcome articles describing novel statistical and computational methods for managing and analyzing experimental data to derive knowledge and modelling physiological processes in several scales. We welcome submissions related to but not limited to the following sub-topics:
• Studies based on the application of statistical and machine learning methods in physiology
• Omics Data integration and database development in physiology
• Mathematical modelling of physiological processes, biochemical and molecular networks
• Computational procedures focused on the characterization of physiological processes.
• Analysis of omics, image or electronic health records in physiological processes with particular focus on data integration
• Text mining applications in physiology
• Biomechanical constitutive data in physiology
• Image biomarkers characterization of physiological processes
The role of mathematical modelling has a long history in physiology as a valuable tool to understand the complexity of the biological function. Typically, this complexity is shaped by the existence of nonlinear relationships, and stochastic and time-varying dynamics. Today, the availability of extensive collections of biomedical and clinical data, together with molecular data thanks to the widespread use of omics technologies, allows researchers to unravel part of this complexity. It is essential to use proper statistical and computational methods to derive knowledge and robust results from these datasets, even generating new estimated values in this context. Disciplines such as biostatistics, bioinformatics or systems biology, sometimes in conjunction with biomechanics, lead the success of this data revolution in the analysis of physiological processes.
This Research Topic is dedicated to cover the development of advanced data analysis methodologies and its application to the analysis of molecular, image, text or clinical data in physiology. We welcome articles describing novel statistical and computational methods for managing and analyzing experimental data to derive knowledge and modelling physiological processes in several scales. We welcome submissions related to but not limited to the following sub-topics:
• Studies based on the application of statistical and machine learning methods in physiology
• Omics Data integration and database development in physiology
• Mathematical modelling of physiological processes, biochemical and molecular networks
• Computational procedures focused on the characterization of physiological processes.
• Analysis of omics, image or electronic health records in physiological processes with particular focus on data integration
• Text mining applications in physiology
• Biomechanical constitutive data in physiology
• Image biomarkers characterization of physiological processes
