Phylogenetic analysis based on Machine Learning Algorithm

PHYLOGENETIC ANALYSIS
USING MACHINE LEARNING
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, Tutors India
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Today's Discussion
OUTLINE
Introduction
Phylogenetic Analysis
Currently available methods for inference
Application of machine learning
Future scope

INTRODUCTION
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.
Contd...

PHYLOGENETIC
ANALYSIS
The study of the evolutionary history of a species or a
group of organisms is known as phylogenetic analysis.
Here, the evolutionary relationship between different
species or organisms having a common ancestor is
represented with the help of branching diagrams.
This diagram is called the phylogenetic tree, which can be
either rooted or unrooted.
Phylogenetic analysis can also be used to study the
relationship between characteristics of an organism,
including genes and proteins.
Contd...

The applications of phylogenetic analysis are numerous.
These include – reconstruction of the ancestral gene for the derivation of extant
genes, study of human disease and epidemiology, interpretation of the evolution of
ecological and behavioural traits, estimation of historical biogeographic
relationships, and many more.
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CURRENTLY
AVAILABLE
METHODS FOR
INFERENCE
Previously, morphological features were used in the
assessment of similarities among species and in
phylogenetic analysis.
It has drastically changed over time. Nowadays, this
analysis uses information extracted from DNA, RNA or
protein.
The generation of a phylogenetic tree involves the
alignment of sequences.
The most widely-used tool for this is the alignment-based
methodology.
Contd...

In this method, the two sequences are stacked in a way to highlight their common
symbols and substrings.
This comparison of sequences helps to identify patterns of shared ancestry between
species.
(Munjal 2019). However, exploiting these large-scale molecular data poses
significant challenges.
One of the most difficult tasks is to develop effective techniques for the extraction of
missing data.
Contd...

The Maximum likelihood or Markov Chain Monte Carlo (MCMC) methods and
probabilistic models of sequence evolution are highly reliable statistical methods used
for the reconstruction of gene and species trees.
Even so, many of these approaches are not scalable enough to study phylogenomic
datasets of hundreds or thousands of genes and taxa.
Thus, the development of a quick and efficient method is the need of the hour (
Bhattacharjee 2020).

APPLICATION OF
MACHINE
LEARNING
Machine learning has found various applications in the
field of technology-driven research.
One such usage of machine learning is in the
inference of the phylogenetic tree.
In a recent study, researchers utilized the machine
learning method to predict the best model for the most
common prediction task: phylogenetic tree
reconstruction for a given collection of sequences
(Abadi 2020).
Contd...

A research study gave a detailed analysis of plant diversity trends to date,
demonstrating that using machine learning to forecast future diversity could be
tremendously beneficial.
They applied machine learning approaches to phylogenetic diversity in vascular plants
(Park 2020). Bhattacharjee et al.,
for the very first time, demonstrated the potential and feasibility of using deep learning
techniques to compute distance matrices.
The study evaluated both matrix factorization (ME) and autoencoder (AE) and aimed to
develop improvised models for better results.
Contd...

They showed that both these methods are reliable and can be applied for handling
large-scale datasets.
They also highlighted the ability of these techniques over the heuristic-based
techniques to automatically learn complicated inter-variable associations.
Their research can also be used as a model for applying machine learning methods to
the phylogenetic analysis (Bhattacharjee 2020).
In another research, a machine learning framework was developed to rank the
neighbouring trees in accordance with their prosperity to increase the likelihood.
Contd...

They applied multiple features and utilized machine learning to improve an optimal
tool. The study suggested specific ways to practice machine learning algorithms in
phylogenetic analysis.
Furthermore, they presented a methodology that can significantly speed up treesearch
algorithms without sacrificing accuracy(Azouri 2021).
A recent review focused on the application of machine learning-based techniques in
the data analysis of the human microbiome.
It provided an insight into the plethora of advantages that machine learning has to
offer over classical methods.
Contd...

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).
Contd...

Contd...

FUTURE SCOPE
Machine learning has found various applications in the
field of technology-driven research.
One such usage of machine learning is in the
inference of the phylogenetic tree.
In a recent study, researchers utilized the machine
learning method to predict the best model for the most
common prediction task: phylogenetic tree
reconstruction for a given collection of sequences
(Abadi 2020).Future scope
Contd...

Contd...

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