Rezumat articol ediţie STUDIA UNIVERSITATIS BABEŞ-BOLYAI

În partea de jos este prezentat rezumatul articolului selectat. Pentru revenire la cuprinsul ediţiei din care face parte acest articol, se accesează linkul din titlu. Pentru vizualizarea tuturor articolelor din arhivă la care este autor/coautor unul din autorii de mai jos, se accesează linkul din numele autorului.

 
       
         
    STUDIA INFORMATICA - Ediţia nr.1 din 2021  
         
  Articol:   CONSTRUCTING UNROOTED PHYLOGENETIC TREES WITH REINFORCEMENT LEARNING.

Autori:  PANNA LIPTÁK, ATTILA KISS.
 
       
         
  Rezumat:  
DOI: 10.24193/subbi.2021.1.03

Published Online: 2021-06-30
Published Print: 2021-06-30
pp. 37-53

VIEW PDF


FULL PDF

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.
 
         
     
         
         
      Revenire la pagina precedentă