AI in Evolutionary Science

Artificial Intelligence (AI) has rapidly transformed various research domains, and evolutionary science is no exception. Historically, evolutionary science has aimed to understand the complexities of life by exploring genetic variations, natural selection, and the adaptation processes that have shaped life on Earth. Traditional methods often involved resource-intensive field studies and laboratory experiments. However, with the advent of AI, new avenues have opened for analyzing vast amounts of biological data and modeling evolutionary processes with unprecedented accuracy and speed. AI technologies such as machine learning, deep learning, and natural language processing are now being applied to simulate evolutionary mechanisms, analyze phylogenetic data, and predict evolutionary outcomes. These methods have not only accelerated the pace of research but have also enabled scientists to uncover patterns and make predictions that were previously inconceivable. As AI continues to advance, its integration into evolutionary science promises to drive significant breakthroughs, enhancing our understanding of biodiversity, ecological dynamics, and evolutionary theory.

The aim of this research topic is to explore the intersection of AI and evolutionary science, fostering a multidisciplinary dialogue that can lead to innovative approaches and applications. By showcasing cutting-edge research and methodologies, this topic seeks to highlight how AI can be harnessed to address longstanding questions in evolutionary science and pave new pathways for discovery.

We welcome a variety of submission types, including original research articles, review papers, methodologies, technology and code, perspectives, and case studies, each contributing to our understanding of the role of AI in evolutionary science. Submissions might explore but are not limited to:

1. Evolutionary Algorithms and Optimization Using AI: Exploring AI-driven optimization techniques inspired by natural evolution, which can be applied to solve complex scientific and engineering problems.
2. AI-Assisted Phylogenetic Inference Models: Utilizing AI models to enhance the analysis and interpretation of phylogenetic trees, aiming to improve the accuracy of evolutionary relationships.
3. Evolutionary Genomic Data Analysis Using AI: Applying machine learning techniques to manage and interpret large genomic datasets, facilitating new insights into evolutionary patterns and genetic diversity.
4. AI in Paleoecological Reconstructions: Leveraging AI to recreate past ecological environments and evolutionary processes, providing a deeper understanding of historical biodiversity
5. AI for Evolutionary Developmental Biology: Implementing AI to model the interactions and regulatory networks that govern the development of organisms and drive evolutionary change.
6. AI-Assisted Predictive Modeling and Evolutionary Dynamics: Using AI to create predictive models that simulate evolutionary dynamics and forecast future evolutionary scenarios under varying environmental conditions.

We aim to build a comprehensive collection of research that not only documents the capabilities and limitations of AI in evolutionary science but also sets the stage for future innovative collaborations between AI specialists and evolutionary scientists.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Community Case Study
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Community Case Study
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review
• Technology and Code

Keywords: Artificial Intelligence, Evolutionary Science, Machine Learning, Phylogenetics, Genomics, Paleoecology, Evolutionary Algorithms, Predictive Modeling

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.