The field of machine learning and deep learning in data analytics and predictive analytics of physiological data has seen significant advancements in recent years. Physiological data, characterized by its complexity and multifaceted nature, often presents challenges in detection and analysis through traditional methods. Standard testing methods frequently fall short in identifying subtle patterns, and the sheer volume of potential predictors can overwhelm conventional analytical approaches. Recent studies have demonstrated the efficacy of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in not only deciphering the intricate features of physiological data but also in predicting future outcomes based on unseen data. Despite these advancements, there remains a critical need for more comprehensive investigations to bridge existing gaps, particularly in integrating AI with cellular and systems physiology to enhance our understanding of physiological and pathological conditions.
This Research Topic aims to explore and expand the application of AI, machine learning, and deep learning in the analysis and prediction of physiological data. The primary objectives include addressing specific questions such as: How can AI and ML improve the detection and interpretation of complex physiological signals? What are the most effective AI-driven methods for predicting physiological states and conditions? Additionally, the research will test hypotheses related to the integration of multi-omics data and the development of novel AI models for translational and clinical research in physiology.
To gather further insights in the realm of AI and machine learning applications in physiological data analytics, we welcome articles addressing, but not limited to, the following themes:
- 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 collection aims to provide a comprehensive platform for innovative studies that combine AI with cellular, molecular, and systems physiology, ultimately contributing to advancements in both basic and translational biomedical research.
The field of machine learning and deep learning in data analytics and predictive analytics of physiological data has seen significant advancements in recent years. Physiological data, characterized by its complexity and multifaceted nature, often presents challenges in detection and analysis through traditional methods. Standard testing methods frequently fall short in identifying subtle patterns, and the sheer volume of potential predictors can overwhelm conventional analytical approaches. Recent studies have demonstrated the efficacy of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in not only deciphering the intricate features of physiological data but also in predicting future outcomes based on unseen data. Despite these advancements, there remains a critical need for more comprehensive investigations to bridge existing gaps, particularly in integrating AI with cellular and systems physiology to enhance our understanding of physiological and pathological conditions.
This Research Topic aims to explore and expand the application of AI, machine learning, and deep learning in the analysis and prediction of physiological data. The primary objectives include addressing specific questions such as: How can AI and ML improve the detection and interpretation of complex physiological signals? What are the most effective AI-driven methods for predicting physiological states and conditions? Additionally, the research will test hypotheses related to the integration of multi-omics data and the development of novel AI models for translational and clinical research in physiology.
To gather further insights in the realm of AI and machine learning applications in physiological data analytics, we welcome articles addressing, but not limited to, the following themes:
- 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 collection aims to provide a comprehensive platform for innovative studies that combine AI with cellular, molecular, and systems physiology, ultimately contributing to advancements in both basic and translational biomedical research.