AMBIENTUM BIOETHICA BIOLOGIA CHEMIA DIGITALIA DRAMATICA EDUCATIO ARTIS GYMNAST. ENGINEERING EPHEMERIDES EUROPAEA GEOGRAPHIA GEOLOGIA HISTORIA HISTORIA ARTIUM INFORMATICA IURISPRUDENTIA MATHEMATICA MUSICA NEGOTIA OECONOMICA PHILOLOGIA PHILOSOPHIA PHYSICA POLITICA PSYCHOLOGIA-PAEDAGOGIA SOCIOLOGIA THEOLOGIA CATHOLICA THEOLOGIA CATHOLICA LATIN THEOLOGIA GR.-CATH. VARAD THEOLOGIA ORTHODOXA THEOLOGIA REF. TRANSYLVAN
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Authors: PANNA LIPTÁK, ATTILA KISS.
DOI: 10.24193/subbi.2021.1.03
Published Online: 2021-06-30
Published Print: 2021-06-30
pp. 37-53
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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 this study, we investigate whether the reinforced learning model of machine learning can generate accurate phylogenetic trees based on the distance matrix.
Keywords and phrases: Bioinformatics, Reinforcement Learning, Machine Learning Algorithms.
2010 Mathematics Subject Classification: 68T05.
1998 CR Categories and Descriptors. code [Artificial Intelligence]: Applications and Expert Systems - Medicine and science.
