A Systematic Review: Deep Learning for Analyzing Genomic Data to Discover Evolutionary Patterns

Deep learning has been increasingly applied to evolutionary genomics as genomic datasets have grown in scale and complexity. However, the literature encompasses heterogeneous biological objectives, modeling assumptions, and evaluation standards, often treated as a unified field despite important conceptual differences. This study presents a systematic review of research published between 2016 and 2025 on the use of deep learning to identify evolutionary patterns in genomic data. Following a structured screening process, 50 studies were selected for qualitative synthesis. The reviewed applications can be organized into three partially overlapping but conceptually distinct domains: (i) population genetic inference, (ii) phylogenetic reconstruction, and (iii) sequence representation learning using DNA and protein language models. In population genetics, deep learning is predominantly employed within simulation-based inference frameworks. In phylogenetics, neural architectures are used to approximate or accelerate tree and model inference under defined conditions. In representation learning, models focus on extracting transferable sequence features for downstream evolutionary or functional analyses. Across domains, deep learning provides flexible modeling of complex genomic inputs. Nevertheless, recurring limitations include challenges in interpretability, sensitivity to training assumptions-particularly under simulation-based settings-heterogeneous evaluation protocols, and substantial computational demands. By organizing the literature using a domain-aligned framework, this review clarifies domain-specific strengths, limitations, and research gaps, providing a structured basis for future methodological development in evolutionary genomics.