The discovery of new molecular signatures and the development of novel biosensors are essential for the diagnosis and treatment of cancer, infectious, neurological, and cardiovascular diseases. Innovations in Biosensing and Biomedicine for these disorders have spurred highly interdisciplinary, data-rich science and been applied in fundamental biology and a variety of clinical studies, such as disease diagnosis, prognosis, patient stratification, and treatment. Machine learning, as a branch of artificial intelligence, has increasingly captivated the imagination of researchers and shown impressive results recently across a wide range of domains in biology and medicine.
Advanced machine learning methods with their ability to integrate vast datasets, incorporate existing knowledge, and learn arbitrarily complex relationships have shown promising advances that could potentially solve many of those most pressing challenges and revolutionize the field of Biosensing and Biomedicine. The recent global research and development in response to the COVID-19 pandemic have further manifested the crucial need to develop the next generation biosensors and medicines for the detection, diagnosis, and treatment of such newly emerging diseases.
Accordingly, this Research Topic seeks to showcase original research papers, mini-review and perspectives that focus on:
• Novel design, fabrication, and modeling of biomolecular markers based on advanced machine learning approaches.
• Machine learning assisted bio-probe development for biosensing
• Innovative imaging, sensing, and biomolecular analysis enabled by machine learning
• Intelligent technology for disease diagnosis/treatment, human health monitoring, clinical data collection, and analysis.
• Machine Learning-aided design and optimization of drug or drug delivery systems for precise and personalized biomedicine.
• Incorporation of machine learning approaches in solving the bottlenecks of biosensing and biomedicine development.
The discovery of new molecular signatures and the development of novel biosensors are essential for the diagnosis and treatment of cancer, infectious, neurological, and cardiovascular diseases. Innovations in Biosensing and Biomedicine for these disorders have spurred highly interdisciplinary, data-rich science and been applied in fundamental biology and a variety of clinical studies, such as disease diagnosis, prognosis, patient stratification, and treatment. Machine learning, as a branch of artificial intelligence, has increasingly captivated the imagination of researchers and shown impressive results recently across a wide range of domains in biology and medicine.
Advanced machine learning methods with their ability to integrate vast datasets, incorporate existing knowledge, and learn arbitrarily complex relationships have shown promising advances that could potentially solve many of those most pressing challenges and revolutionize the field of Biosensing and Biomedicine. The recent global research and development in response to the COVID-19 pandemic have further manifested the crucial need to develop the next generation biosensors and medicines for the detection, diagnosis, and treatment of such newly emerging diseases.
Accordingly, this Research Topic seeks to showcase original research papers, mini-review and perspectives that focus on:
• Novel design, fabrication, and modeling of biomolecular markers based on advanced machine learning approaches.
• Machine learning assisted bio-probe development for biosensing
• Innovative imaging, sensing, and biomolecular analysis enabled by machine learning
• Intelligent technology for disease diagnosis/treatment, human health monitoring, clinical data collection, and analysis.
• Machine Learning-aided design and optimization of drug or drug delivery systems for precise and personalized biomedicine.
• Incorporation of machine learning approaches in solving the bottlenecks of biosensing and biomedicine development.