Artificial Intelligence for Evolutionary Genomics: From Sequences to Systems

Dear Colleagues,

Integrating artificial intelligence (AI) into bioinformatics has revolutionized the study of the prediction of protein sequences, molecular interactions, drug discovery, comparative genomics, etc. However, the existing literature reveals a significant lack of resources or methodologies for effectively applying AI in phylogenetic studies—an area where AI could unlock unprecedented insights into genetic diversity, adaptation, and phylogenetic relationships.

AI-driven approaches, particularly machine learning (ML) and deep learning (DL), are transforming large-scale genomic data analysis by enabling precise predictions of evolutionary patterns, functional genomic elements, and selective pressures. By leveraging advanced computational models, researchers can now decode hidden evolutionary signals, reconstruct ancestral genomes, and identify key genetic drivers of speciation and adaptation.

This Special Issue, Artificial Intelligence for Evolutionary Genomics: From Sequences to Systems, highlights cutting-edge AI applications in genome evolution, phylogenomics, and cross-species genomic analyses. We welcome original research articles, reviews, and methodological studies that harness AI for challenges such as sequence alignment, ortholog prediction, phylogenomic inference, evolutionary dynamics, and genomic prediction. Submissions addressing critical challenges, such as model interpretability, scalability for large datasets, and multi-omics integration, are particularly encouraged.

By bridging AI and bioinformatics, this Special Issue will accelerate discoveries in molecular evolution, refine comparative genomic methodologies, and pioneer innovative tools for unravelling the genetic foundations of biodiversity.

Topics of interest include, but are not limited to, the following:

• AI-based methods for sequence alignment and homology detection;
• ML and DL methods for ortholog/paralog identification;
• Metrics for evaluating phylogenetic classification;
• Frameworks for comparing AI-generated trees or alignments with reference data;
• Tree reconstruction using AI-based models.

Dr. Lingling Jin
Dr. Nadia Tahiri
Guest Editors

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