phylogenetic analysis based on machine learning algorithm
DESCRIPTION
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: 🌐: www.tutorsindia.com 📧: [email protected] 💬(WA): +91-8754446690 🇬🇧(UK): +44-114352002TRANSCRIPT
An Academic presentation by Dr. Nancy Agnes, Head, Technical Operations, Tutors India Group www.tutorsindia.comEmail: [email protected]
PHYLOGENETIC ANALYSISUSING MACHINE LEARNING
Introduction
Phylogenetic Analysis
Currently available methods for inference
Application of machine learning
Future scope
OUTLINE
Today's Discussion
INTRODUCTION The interpretation of the phylogenetic tree is anessential yet challenging aspect of evolutionary studies.
To conduct an evolutionary study of the organisms is thecore of biological research.
The resulting phylogeny is then subjected to a plethora ofanalyses essential for further genomic research (Azouri2021).
The phylogenetic analysis involves several methodsthat can be used to interpret data. Recently, researchershave begun studying the use of machine learning ininferring phylogenetic trees.
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PHYLOGENETICANALYSIS The study of the evolutionary history of a species or a
group of organisms is known as phylogenetic analysis.
Here, the evolutionary relationship between differentspecies or organisms having a common ancestor isrepresented with the help of branching diagrams.
This diagram is called the phylogenetic tree, which can beeither rooted or unrooted.
Phylogenetic analysis can also be used to study therelationship between characteristics of an organism,including genes and proteins.
The applications of phylogenetic analysis are numerous.
These include – reconstruction of the ancestral gene for the derivation of extantgenes, study of human disease and epidemiology, interpretation of the evolution ofecological and behavioural traits, estimation of historical biogeographicrelationships, and many more.
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CURRENTLYAVAILABLEMETHODS FORINFERENCE
Previously, morphological features were used in theassessment of similarities among species and inphylogenetic analysis.
It has drastically changed over time. Nowadays, thisanalysis uses information extracted from DNA, RNA orprotein.
The generation of a phylogenetic tree involves thealignment of sequences.
The most widely-used tool for this is the alignment-basedmethodology.
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In this method, the two sequences are stacked in a way to highlight their commonsymbols and substrings.
This comparison of sequences helps to identify patterns of shared ancestry betweenspecies.
(Munjal 2019). However, exploiting these large-scale molecular data posessignificant challenges.
One of the most difficult tasks is to develop effective techniques for the extraction ofmissing data.
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The Maximum likelihood or Markov Chain Monte Carlo (MCMC) methods andprobabilistic models of sequence evolution are highly reliable statistical methods usedfor the reconstruction of gene and species trees.
Even so, many of these approaches are not scalable enough to study phylogenomicdatasets 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).
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field of technology-driven research.
One such usage of machine learning is in theinference of the phylogenetic tree.
In a recent study, researchers utilized the machinelearning method to predict the best model for the mostcommon prediction task: phylogenetic treereconstruction for a given collection of sequences(Abadi 2020).
APPLICATION OFMACHINELEARNING Machine learning has found various applications in the
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A research study gave a detailed analysis of plant diversity trends to date,demonstrating that using machine learning to forecast future diversity could betremendously 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 learningtechniques to compute distance matrices.
The study evaluated both matrix factorization (ME) and autoencoder (AE) and aimed todevelop improvised models for better results.
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They showed that both these methods are reliable and can be applied for handlinglarge-scale datasets.
They also highlighted the ability of these techniques over the heuristic-basedtechniques to automatically learn complicated inter-variable associations.
Their research can also be used as a model for applying machine learning methods tothe phylogenetic analysis (Bhattacharjee 2020).
In another research, a machine learning framework was developed to rank theneighbouring trees in accordance with their prosperity to increase the likelihood.
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They applied multiple features and utilized machine learning to improve an optimaltool. The study suggested specific ways to practice machine learning algorithms inphylogenetic analysis.
Furthermore, they presented a methodology that can significantly speed up tree-search algorithms without sacrificing accuracy(Azouri 2021).
A recent review focused on the application of machine learning-based techniques inthe data analysis of the human microbiome.
It provided an insight into the plethora of advantages that machine learning has tooffer over classical methods.
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The most common techniques covered in this review involved Support VectorMachines, Random Forest, k-NN and Logistic Regression.
This review suggested how machine learning can contribute to the development ofnew models that can be useful in predicting classifications in the field of microbiology,inferring host phenotypes to predict diseases and characterization of state-specificmicrobial signatures using microbial communities(Macros 2021).
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FUTURE SCOPE
Machine learning has found various applications in thefield of technology-driven research.
One such usage of machine learning is in theinference of the phylogenetic tree.
In a recent study, researchers utilized the machinelearning method to predict the best model for the mostcommon prediction task: phylogenetic treereconstruction for a given collection of sequences(Abadi 2020).Future scope
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