Artificial Intelligence and Its Importance in Genomics [ AI ]

AI in Genomics

Importance of AI in Genomics.

Artificial Intelligence is a fairly new but highly appreciated field of Technology today. With the availability of data getting larger every day the importance of such fields is only increasing. Privacy is no sustaining nowadays, thanks to the advancements in the field of technology. Nowadays tech giants have access to your personal taste, habits online, your preferences and sometimes, even your financial history. If I tell you that your own genetic code might get under the microscope, how does it sounds?

 Interesting right??

 

Scientist these days are seeing more and more scope for  AI implementation in the field of genomics and they have successfully tested their theories and are now ready to apply them. The research of DNA is definitely a serious matter since scientists can get valuable data that will basically tell them exactly how a certain organism functions. And Artificial intelligence technologies can do all that faster cheaper and more effective. With all the collected insights, we can make better decisions on someone’s treatment, spot future possible mutations as well as predict future possible vulnerabilities. There is no doubt that AI has a lot to contribute to this field and its application in genomics is indispensable.

 

Present Challenges:-

  1. Extracting location and structure of genes.
  2. Identifying regulatory elements.
  3. Identifying non-coding RNA genes.
  4. Gene function prediction
  5. RNA secondary structure prediction.

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AI and Genome sequencing

AI in Genome Sequencing
AI in Genome Sequencing

AI in Genomics:   Since our life cycle as well as personal disease and vulnerabilities are heavily determined by Genetics understanding human genetic makeup has become one of the bigger focuses in the sector. The amount in the complexity of all the data that needs to be evaluated has told this process for years. It is only with the help of modern artificial Technology programs as well as machine learning applications we can finally moved this rock from the dead spot. Nowadays researchers can interpreted react on genomic data through genomics Sequencing and gene editing.

Genetic editing is a method of making specific adjustments to DNA at the cellular or organism levels. Notable gene editing technologies include CRISPR, which offers a cheaper and more effective way of conducting gene editing. To select a target sequence, advanced machine learning of the program helps researchers, which is one of the most challenging and important steps in gene editing.

 

Genome sequencing, also known as the whole genome sequencing (WGS) is ostensibly the process of determining the complete DNA sequence of an organism’s genome at a single time. Companies like Deep Genomics , use AI and machine learning in order to help their specialists to interpret many genetic variations. The Algorithms are formed on the basis of identification of the patterns which are formed in the genetics data set. The sets are  getting translated straight to computer models to help clients understand how these genetic variations influence their cellular processes, like DNA repair, hair growth and even metabolism. And any negative impact to the normally functioning pathways can lead to different complex diseases.

 

AI in Genomics
Challenges of AI in Genetics

 

Precautions

There is no doubt that AI is going to play a very important role in the field of genetics for the years to come but necessary precautions must be taken in order to avoid unwanted results. As much interesting it sounds to put your genes under a microscope it is equally disturbing and can lead to huge non-reversible changes. Using AI for genetic mutation can be dangerous and deadly for the whole human race and hence precautions are evident move further in this field. We should be extremely careful of what we do because the results can be hard to change.

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Conclusion : –

The promise of machine learning and AI in Genomics is enormous if it could be fulfilled to the fullest. It could mean near perfect diagnosis optimize medication and treatment choices identified high-risk patients, accurately predicted readmissions and general empowerment of the personalization of medicine wealth all the time seeing total cost minimized. But all the necessary precautions must be taken care of and all the results must be kept in mind before moving any step further in this field. This is precisely the future we all want and hope you reach there. 

 

 

Video Credits: The Artificial Intelligence Channel

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