The tree reconstruction game: phylogenetic reconstruction using reinforcement learning. Dana Azouri (TAU)

The tree reconstruction game: phylogenetic reconstruction using reinforcement learning
Dana Azouri

The following two fields have never interacted before: reinforcement learning and molecular evolution. Here we develop a reinforcement-learning algorithm to tackle the challenge of reconstructing phylogenetic trees, which are used to describe the evolutionary relationships among a set of genome sequences. When only a few dozen of sequences are analyzed, billions of alternative phylogenetic trees could potentially describe the evolutionary patterns, thus rendering the search for the tree that best describes the data algorithmically challenging. Thus, all current algorithms for phylogenetic tree reconstruction use various heuristics to make tree inference feasible. Although recent studies have shown the potential of harnessing AI-based methods to reconstruct phylogenetic trees, the question regarding the overall tree-search strategy remains open. In this study, we provide a novel paradigm for predicting the maximum-likelihood tree by using reinforcement-learning techniques to learn an optimal strategy for tree search. We demonstrate the use of the developed deep-Q-learning agent on a set of unseen empirical data, namely, on unseen RL environments defined by nucleotide MSAs of up to 20 sequences. Our results show that the likelihood scores of the inferred phylogenies is as accurate as those obtained from widely-used software. It thus establishes a proof-of-concept that it is beneficial to optimize a sequence of moves in the search-space, rather than only optimize the progress made in every single move. This suggests that an RL-based method provides a promising direction for phylogenetic reconstruction.

BIO:

I am Dana Azouri, a computational biology researcher at TAU, with a passion for data and for science. I am interested and experienced in the broad field of theoretical evolution and phylogenomics, Currently finalizing my PhD thesis and working on developing machine-learning frameworks to model the genome evolution. During my PhD I lectured Python and data science for biology graduates and lead a graduate students’ community which is in attendance of over 200 students. My supportive PhD supervisors who guide me throughout my research projects are Prof. Itay Mayrose and Prof. Tal Pupko. I also had the honor to receive much brilliant advice from Prof. Yishay Mansour. The main scholarships and awards that support my work are the Higher Education program for excellent PhD students in Data Sciences (VTAT; PBC fellowship); the fellowship of the Fast & Direct PhD Honors Program; the excellence in research award by the TAU Life-Sciences faculty; the excellence in research award by the Edesman Foundation; the TAU Life Science faculty award for excellent achievements in teaching.

https://www.linkedin.com/in/dana-azouri-982b14178/
Dana.