About this Research Topic
This featured collection is aimed at highlighting groundbreaking explorations at the crossroads of Machine Learning, Artificial Intelligence (AI) and biological models, leveraging state-of-the-art technologies to propel advancements in the life sciences. As AI continues its transformative journey across diverse sectors, its integration into biological research holds immense potential for unraveling the intricacies of living systems and expediting scientific breakthroughs. Central to the collection is a commitment to interdisciplinary collaboration and innovations at the frontier of biological research, ultimately improving human health, agricultural knowledge and well-being. Studies to be included in this collection are those involving a meticulous and inclusive exploration of genetic, molecular, biochemical, physiological, and morphological data representing both the stationary and fluctuating biological functions within an organism as a unified entity. Convergent studies that integrate biology, mathematics, computer science, and engineering, are important to decipher the intricate networks of interactions that govern biological systems. Featured concepts in Machine learning, computational modeling, data analysis, and experimental validation, will help unravel the complexities of biological phenomena at various scales, from molecular interactions within cells to the emergent properties of organisms.
Articles in this collection will culminate in broad representation of new avenues for understanding living systems and tackling pressing biomedical challenges.
For this collection, we welcome papers that address broad topics that harnesses the power of AI methodologies to deepen our understanding of biological phenomena. Below are some examples of research areas that would be welcome in this collection;
● Studies utilizing machine learning algorithms to analyze genomic data and predict genetic predispositions to diseases.
● Demonstrations of how AI can enhance our comprehension of complex biological processes and inform personalized precision medicine approaches.
● Agriculture and Environmental implications of AI and Machine Learning.
● Neural networks and behavior of neurons in the brain to better understand neurological disorders like Parkinson's disease.
● Development of AI-driven models to simulate protein folding, aiding in drug discovery and the development of targeted therapies.
Articles in this collection will culminate in broad representation of new avenues for understanding living systems and tackling pressing biomedical challenges.
For this collection, we welcome papers that address broad topics that harnesses the power of AI methodologies to deepen our understanding of biological phenomena. Below are some examples of research areas that would be welcome in this collection;
● Studies utilizing machine learning algorithms to analyze genomic data and predict genetic predispositions to diseases.
● Demonstrations of how AI can enhance our comprehension of complex biological processes and inform personalized precision medicine approaches.
● Agriculture and Environmental implications of AI and Machine Learning.
● Neural networks and behavior of neurons in the brain to better understand neurological disorders like Parkinson's disease.
● Development of AI-driven models to simulate protein folding, aiding in drug discovery and the development of targeted therapies.
Keywords: machine learning, artificial intelligence, biological research, Predictive Models
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.
