Let's discuss about trends/facts of AI in Genomics.

What is Artificial Intelligence (AI)?                      Artificial intelligence is the simulation of human intelligence processes by machines, especially computer system. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.    

  What is Genomics in AI?                                    The genomics field continues to expand the use of computational methods such as artificial intelligence and machine learning to improve our understanding of hidden patterns in large and complex genomics data sets from basic and clinical research projects. Genomics is a branch of science that studies genes and their function, structure, evolution, and mapping of genomes in short organisms.

Facts Of AI In Genomics:                            Although the use of AI/ML tools in genomics is still at an early stage, researchers have already benefited from developing programs that assist in specific ways.

Some examples include:

• Examining people’s faces with facial analysis AI programs to accurately identify genetic disorders.
• Using machine learning techniques to identify the primary kind of cancer from a liquid biopsy.
• Predicting how a certain kind of cancer will progress in a patient.
• Identifying disease-causing genomic variants compared to benign variants using machine learning.
• Using deep learning to improve the function of gene editing tools such as CRISPR.                   Current applications of machine learning in genomics appear to fall under the following two categories:
• Genome sequencing (particularly as it applies to precision medicine): Researchers are using machine learning to identify patterns within high volume genetic data sets. These patterns are then translated to computer models which may help predict an individual’s probability of developing certain diseases or help inform the design of potential therapies.
• Direct-to-Consumer genomics: This category encompasses companies who offer genomic sequencing services to individual consumers. Companies are using machine learning to achieve greater depth in the interpretation of genetic information such as how an individual’s genes may impact their weight.        

We can divide genomics into several subsets: regulatory genomics, structural genomics, and functional genomics.

• Regulatory genomics – it's the study of genomics features and ways of regulating expression. For example, machine learning applications in this area include predicting classifying gene expression, producing transcription factors and RNA-binding proteins, or using ML tools to predict promoters and enhancers for gene expression.
• Functional genomics – in this area, researchers attempt to describe gene functions and interactions. Machine learning can potentially help in classifying mutations in functional activity, producing promoters and enhancers, and classifying subcellular localization.
• Structural genomics – this is where researchers explore the characterization of genome structures. Machine learning can help to classify structures of proteins, classify protein tertiary structure, and make connections about protein second

Machine learning applications in genomics today

1. Gene editing

Gene editing refers to a selection of methods for making alterations to the DNA at the cellular or organism level. One of the recent advances in the field is CRISPR, a gene-editing technology that offers a faster and cheaper way of carrying out such projects. However, to use CRISPR researchers need to select the right target sequence first. And this process can be very challenging as it often involves unpredictable outcomes. Machine learning offers a glimmer of hope. The technology might significantly reduce the cost, time, and effort it takes to identify the right target sequence. 

For example, a London-based company called Desktop Genetics works at the intersection of artificial intelligence and CRISPR. The company loads experimental or reference data to Google Cloud and then formats and processes it before it's moved to bioinformatics teams. With the help of this data, researchers can analyze and design CRISPR experiments or train new models. Machine learning will impact CRISPR even more as new techniques are discovered and implemented.

2. Genome sequencing

Another area where machine learning is causing disruptions is genome sequencing, a recent field of interest in medical diagnostics. It includes modern DNA sequencing techniques that allow researchers to sequence the entire human genome in one day. The classic sequencing technology required more than a decade for completion when the human genome was sequenced first. Talk about innovation! 

Companies like Deep Genomics are now operating on the market and using machine learning to help researchers interpret genetic variation. In particular, development teams design algorithms based on patterns identified in large genetic data sets. These patterns are then translated to computer models that help researchers to interpret how genetic variation affects critical cellular processes like metabolism, cell growth, or DNA repair. Disruption to the normal functioning of these processes can potentially cause diseases like cancer. That's why using machine learning in genomics research is so important.

3. Clinical workflows

This area of the medical industry stands to benefit a lot from machine learning as well. Here's an example scenario: take a look at any healthcare system, and you'll find a platform that includes patient data. However, it's common to find gaps in the patient data and their availability to different members of the healthcare team. Machine learning can help to increase the efficiency of the clinical workflow process. 

Intel has recently released an Analytics Toolkit that puts together machine learning capabilities with the clinical workflow processes. Intel partnered with the Transformation Lab at the Intermountain Healthcare in Salt Lake City, Utah, to efficiently integrate genomics in the institution's breast cancer treatments and patient care. 

This partnership allowed for the development of an algorithm that measures factors such as the patient's level of risk for developing different types of cancer. The company was able to develop a workflow that enables sharing data easily and making the most of the available patient data.

4. Consumer genomics products

Genetic testing and consumer genomics are becoming an increasingly important market for innovation. The anticipated market expansion of these areas is powered by the growing awareness among societies of how genetic tests can be used to determine the likelihood of developing a particular disease. Companies such as 23AndMe or Ancestry.com are becoming household names among consumers. 

Future applications of machine learning in genomics

1. Pharmacy genomics

Pharmacy genomics is an emerging field within precision medicine that examines the role of genomics in the context of an individual response to particular drugs. This area is a quickly developing one but still relatively new. However, researchers are already experimenting with machine learning techniques. For example, we have seen studies where machine learning models were applied to determine a stable dose of a particular drug in renal transplant patients. 

In the future, researchers will be using machine learning models to better understand the individual response to particular treatments and, as a result, create more personalized treatments.

2. Genetic screening of newborns

Some experts believe that newborn genetic screening might become a standard practice during the next decade. The idea is to collect data birth and then integrate it into the individual EHR profile. Another facet of this trend is making noninvasive screening capabilities available to women during pregnancy. These would be geared at identifying particular diseases such as Down syndrome. 

The Newborn Screening Center at the National Taiwan University Hospital used machine learning to increase the accuracy of their web-based newborn screening system. The focus here was metabolism defects. A study showed that machine learning helps researchers to successfully reduce the number of false positives significantly for various diseases.

3. Agriculture

Let's not forget that genomics is a discipline relevant to our food production industry. Experts imagine that in the future machine learning will be helping farmers to improve soil quality and crop yield. The California-based startup PathoGn combines genomics and machine learning to create diagnostic tools for preventing and predicting diseases and crops. Today the startup is called Trace Genomics and focuses more on soil health. 

However, we can easily imagine using genetic data to predict the health of crops so that farmers can better predict and optimize yields. On a large scale, such innovations could lead to significant global improvements in crops and solve world problems such as hunger.      

Conclusion:                                                                          The combination of artificial intelligence technologies such as machine learning and genomics can potentially solve several significant problems we are facing today. With powerful machine learning algorithms, genomics researchers will be able to deliver better results faster, at lower cost - making their outcomes available to more people in the future.