Phylogenetic analysis based on Machine Learning Algorithm
The interpretation of the phylogenetic tree is an essential yet challenging aspect of evolutionary studies. To conduct an evolutionary study of the organisms is the core of biological research. The resulting phylogeny is then subjected to a plethora of analyses essential for further genomic research (Azouri 2021). The phylogenetic analysis involves several methods that can be used to interpret data. Recently, researchers have begun studying the use of machine learning in inferring phylogenetic trees. Contact:
The interpretation of the phylogenetic tree is an essential yet challenging aspect of evolutionary studies. To conduct an evolutionary study of the organisms is the core of biological research. The resulting phylogeny is then subjected to a plethora of analyses essential for further genomic research (Azouri 2021). The phylogenetic analysis involves several methods that can be used to interpret data. Recently, researchers have begun studying the use of machine learning in inferring phylogenetic trees.
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The most common techniques covered in this review involved Support Vector
Machines, Random Forest, k-NN and Logistic Regression.
This review suggested how machine learning can contribute to the development of
new models that can be useful in predicting classifications in the field of microbiology,
inferring host phenotypes to predict diseases and characterization of state-specific
microbial signatures using microbial communities(Macros 2021).
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