10 Applications of Machine Learning in Genomics

You and me, we all have genes that constitute our biological bodies and define us the way we are. Our facial features, our behavior, and our genetics are all defined by the study of our genes under the study of genomics. 

 

The study of genomics, a field of life sciences, includes mapping, function, evolution, and editing of genomes that define our genetic constitution. 

 

As opposed to genetics which focuses on individual genes and their influence on our existence, genomics focuses on the bigger picture, taking into consideration the collective group of genomes, their interaction with each other, and how their modification transforms our genomic nature. 

 

The discussion on the existence of genomes dates back to the year 1926. However, the study of genomics has only been the talk of the town in recent decades with the rise of technology and the evolution of genome sequencing and analysis. 

 

The foundation of this concept was laid by DNA sequencing which triggered the rise of debates revolving around how DNA affects our biological nature and the way we are related to our birth givers. 

 

While a lot of you might confuse genomics with genetics, it is important to note that these two fields are quite distinct from each other. 

 

Despite scientific advancements in the field of genome sequencing, one should not forget that it was a technological advancement that triggered the development of genomics as a separate focus of research. With the help of AI and machine learning, genomics has leaped forward by helping in medicine and other biological fields of study. 

 

Therefore, it is important to understand how machine learning algorithms are influential when it comes to genomics and how the field of science has benefited from technology. 

 

Applications of Machine Learning in Genomics

 

While machine learning has broadly influenced the way genome sequencing is done, it goes beyond the basics and has undoubtedly been a cornerstone for development in this field. Let us discover the top 10 applications of machine learning in genomics. 

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Applications of Machine Learning in Genomics

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1. Genome Sequencing

 

The topmost application of machine learning tools and techniques in genomics revolves around genome sequencing. Simply put, genome sequencing aims to determine the biological nature of an organism’s genome. 

 

Scientists believe that genome sequencing is the key to determining particular diagnoses and treatments for particular genomes using this technology. With the help of ML, genome sequencing can be made a lot easier as trained models are put to use. 

 

These ML models are capable of focusing on the important parts of the entire genetic makeup of an organism, aiding in focused discoveries about biology. 

 

A plethora of machine learning methods have come to the fore to help us in discerning how gene expression works. Some of the techniques aim at predicting the expression of a gene exclusively on the basis of the DNA sequence. 

 

2. Predictive Testing

 

The second application of ML in genomics is predictive testing. Since genomics largely deals with studying the genetic constitution of an organism, it is certainly capable of exposing underlying factors of disease among a group of organisms belonging to the same species. 

 

This calls for predictive analytics that can help scientists to identify, analyze, and forecast certain diseases in an organism on the basis of its past record and underlying biological factors.  

 

Scholars claim that by the next decade, predictive genome analysis will expand the genomics market manifold and this will witness a rise in the application of breakthrough technologies like machine learning and artificial intelligence. 

 

3. Pharmacogenomics

 

One of the biggest advantages of machine learning is that it can experiment with the present factors to test future situations without altering reality. That said, pharmacogenomics is a combined study of pharmacology and genomics that explains how a certain genome will react to a particular drug.

 

With the help of machine learning algorithms, drugs can be modified as per genome suitability and then delivered to customers for better results. Even though pharmacogenomics has a long way to go, this concept has already been introduced as a suitable application of machine learning in genomics. 

 

By using ML tools, scientists can experiment with drug compositions and research accordingly so that the results are better and more plausible. 

 

This was the foundation of constituting suitable vaccines and drugs for Covid-19 that shook the entire world. With the help of genomics, pharmacists were able to constitute drugs ideal for the genomes of humans and other organisms.  

 

4. Data Clustering

 

To carry out any experiment, research, or even a laboratory process in genomics, data is essential. Huge amounts of data are recorded alongside records that help scientists to understand the nature of studies that have already been carried out. 

 

However, with so much data comes chaos that can clutter the aim of future studies. Thus, in order to segregate data effectively and perform data management, machine learning is one of the best approaches. 

 

Not only does machine learning aid in data clustering, but it also supervises the way new data is added to different clusters and other such specifics. 

 

With the coming of ML, data organization requires less effort and can be stored for eternity in customized software unlike traditional times wherein scientists had to access record rooms for the same. It is safe to say that times have changed and so has the face of technology. 

 

5. Genetic Disorders

 

Yet another use of machine learning arises when scientists need to identify genetic disorders and dig deep into genomics. Genomics can very well help scientists to identify why people from a certain kind of genome are more prone to a certain kind of disease. 

 

This calls for identifying genetic disorders and how entire genomes interact with environmental factors to give rise to various disorders. A typical example of this situation can be a family developing a disease when they shift to a different continent with a different climate. 

 

This means that the family’s genome is prone to cooler/hotter climates as compared to the one that they were living in. Herein, machine learning can help scientists to identify similar cases of patients that experienced similar symptoms. 

 

What’s more, breakthrough revelations about such disorders can be released for worldwide welfare so that migrants are well aware of their vulnerabilities and can take care of themselves beforehand. 

 

6. Genetic Research Studies

 

Another use of ML in the field of genomics is the way scientists conduct genetic and genomic research studies. To begin with, we shall first understand the way genomics can be influenced by revolutionary research projects. 

 

Scientists believe that genomics and genetics help us to understand why a certain disease occurs in a group of people belonging to a certain genome. This also includes specific genes that might influence the arrival of diseases. 

 

To feed this curiosity, machine learning software can be employed to register data, analyze diseases, find the root cause, and yield results with a reliable foundation. 

 

7. Gene Modification

 

Ever heard of gene editing in humans? Well, gene modification or editing is concerned with altering bits of DNA sequence to obtain desired traits of features in an organism. 

 

This can be conducted at both the genetic and genomic levels. By putting machine learning to use, gene editing can be carried out without errors or compromising the quality of the results. 

 

In addition, it also allows the experimenter to be thorough with the process before performing the same process in reality. 

 

Thanks to ML, genomics has advanced substantially without requiring any assistance from animal experimentation. Artificially created DNA can become the basis of such studies and be altered accordingly to reap the benefits in the best possible way. 

 

Machine learning provides the capacity to minimize the cost, time and the effort required to determine a convenient target sequence.

 

8. Gene Ontology

 

In biology, gene ontology refers to the aspect of genetic molecular activity that helps researchers study each and every aspect of the biological makeup closely. 

 

This concept, with the help of renowned technologies like machine learning, minutely watched the way genetic behavior can be generalized and represented via genomics. 

 

Broadly, gene ontology analysis ranges across 3 factors in genetic makeup of an organism - molecular function, cellular component, and biological process. This dynamic process helps scientists to formulate a graphical structure of an organism’s makeup that further leads to in-depth study. 

 

A typical example in this field might be discovering a new species and studying its genomics to unfold hidden secrets about its DNA structure, molecular activities, and biological capabilities. 

 

9. Principal Component Analysis

 

In machine learning, Principal Component Analysis (PCA) is a useful approach that helps visualize multidimensional data. 

 

With the help of this approach, complex studies can be broken down into easier components to analyze the entire structure in an efficient manner. That said, a complex and crucial field like genomics can be made substantially better to understand and interpret using PCA. 

 

Not only does it provide aid in singularly studying a topic but it simultaneously trains machines to do the task for humans. This saves time, cost, and resources for humans to invest in genomics and other related processes. 

 

10. Genetic Algorithm Study

 

The father of evolution, Charles Darwin proposed the theory of genetic algorithm that reflects the theory of ‘survival of the fittest’. 

 

This led to genetic algorithm studies that today aim to compose an organism that carries the best of its abilities and is proven to survive in the worst conditions on Earth. 

 

By collaborating with machine learning, genetic algorithm studies can be usefully applied to genomics and a set of genomes can be tested, trained, or even artificially created. 

 

This will only lead to a practical approach to the genetic algorithm theory that was originally proposed by Charles Darwin and has served as the basis of numerous studies in the fields of genetics, genomics, and human evolution. 

 

Also Read | What is Genetic Engineering?

 

To sum up, genomics is a complex field wherein machine learning has numerous applications. With this duo put to use, the world can surely witness many otherwise impossible revelations and areas of research.