Phylogenetic Tree Generation and Evolutionary Analysis Using Deep Learning
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Presented innovative research on phylogenetic tree construction using deep learning approaches. Introduced the BioTree framework that combines language models, graph neural networks, and evolutionary algorithms for inferring phylogenetic relationships from molecular sequences. Discussed the MDTree (Masked Dynamic Autoregressive Model) for phylogenetic inference and its advantages over traditional maximum likelihood and Bayesian methods. Covered applications in viral evolution tracking, bacterial taxonomy, and cancer phylogenetics. Demonstrated significant improvements in accuracy and computational efficiency for large-scale phylogenetic studies.