About this Research Topic
Complexity is an intrinsic feature of physiological data where results are often interpreted based on individual chemical and physical functions, and thus it is prone to two main limitations: failure of detection through standard testing, and lack of capacity to include a large number of potential predictors. Artificial intelligence, machine learning, and deep learning have been used effectively not only to understand diverse features of complex physiological data but also to predict and forecast likely future outcomes on unseen data. In the past few years, there was an exponential growth in implementing AI and machine learning in basic, translational and clinical research. In this Research Topic, we seek to facilitate the communication of up-to-date knowledge on AI in cellular and systems physiology. This Research topic will focus on AI and machine learning tools in exploratory analysis of multi-featured physiological data and in and prediction of physiological states and conditions in health and disease. This is closely connected with Frontiers in Network Physiology scope on how diverse physiological components interact during physiological and pathological conditions.
This Research Topic provides a platform for AI-powered basic, translational, and clinical biomedical research by featuring recent innovations made by applying AI and machine learning in physiology, in particular cellular, molecular, and systems physiology. The focus of this topic is to create a collection of high-quality papers of innovative studies combining artificial intelligence, cellular and molecular physiology, and systems physiology. This Research Topic will be of high interest for Frontiers in Network Physiology readers as it focuses on the use of AI and machine learning tools in data analysis and defining new features in physiological data as well as model prediction which can assist translational and clinical research in the field of physiology.
This Research Topic on ”Machine Learning and Deep Learning in Data Analytics and Predictive Analytics of Physiological Data” focuses on basic and translational research targeting AI and machine learning in physiological and pathophysiological states.
In this Research Topic we gather high-quality papers including Original Research, Review, Mini Review, and commentary articles covering research in machine learning and deep learning in molecular, cellular ,and systems biology.
The scope of this issue Topics of interests encompass but are not limited to:
Supervised and unsupervised learning for physiological data
Machine learning and deep learning for prognosis and diagnosis
Deep learning for cellular activity
Deep learning for systems biology
Machine learning in molecular biology and physiology
Machine learning for multi-omics data
This Research Topic provides a platform for AI-powered basic, translational, and clinical biomedical research by featuring recent innovations made by applying AI and machine learning in physiology, in particular cellular, molecular, and systems physiology. The focus of this topic is to create a collection of high-quality papers of innovative studies combining artificial intelligence, cellular and molecular physiology, and systems physiology. This Research Topic will be of high interest for Frontiers in Network Physiology readers as it focuses on the use of AI and machine learning tools in data analysis and defining new features in physiological data as well as model prediction which can assist translational and clinical research in the field of physiology.
This Research Topic on ”Machine Learning and Deep Learning in Data Analytics and Predictive Analytics of Physiological Data” focuses on basic and translational research targeting AI and machine learning in physiological and pathophysiological states.
In this Research Topic we gather high-quality papers including Original Research, Review, Mini Review, and commentary articles covering research in machine learning and deep learning in molecular, cellular ,and systems biology.
The scope of this issue Topics of interests encompass but are not limited to:
Supervised and unsupervised learning for physiological data
Machine learning and deep learning for prognosis and diagnosis
Deep learning for cellular activity
Deep learning for systems biology
Machine learning in molecular biology and physiology
Machine learning for multi-omics data
Keywords: Network Physiology, Machine Learning, Deep learning, prediction model, physiology, systems biology, cellular and molecular biology, Data processing
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.
