Constructing Unrooted Phylogenetic Trees with Reinforcement Learning
With the development of sequencing technologies, more and more amounts of sequence data are available. This poses additional challenges, such as processing them is usually a complex and time-consuming computational task. During the construction of phylogenetic trees, the relationship between the sequences is examined, and an attempt is made to represent the evolutionary relationship. There are several algorithms for this problem, but with the development of computer science, the question arises as to whether new technologies can be exploited in these areas of computational biology.
In the following publication, we investigate whether the reinforced learning model of machine learning can generate accurate phylogenetic trees based on the distance matrix.
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