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Artificial Intelligence Genetics Important

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Artificial Intelligence Genetics Important

Artificial Intelligence sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.

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More in detail, Kaplan and Haenlein define AI as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation

Artificial Intelligence (AI) have shown prominent results in various fields, including the field of healthcare. And the uses of the technology today seem countless – from treatment support and automating different healthcare processes to risk prediction and DNA aging alterations. Frost and Sullivan recently predicted that artificial intelligence systems will generate $6.7 billion in revenue from healthcare globally by the year 2021.
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Nowadays companies have access to your personal tastes, habits, online preferences and sometimes, even, your financial history.

And one healthcare sector in which machine learning is currently rapidly evolving is genomics, which is a branch of molecular biology focused on studying all aspects of a genome, as well as the complete set of genes within a specific organism. The spotlight of AI implementation in genomics is primarily made for the human health of course, but it can also become a sensation in animal husbandry and agriculture.

The research of DNA is 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 insight, we can wake better decisions on someone’s treatments, spot future possible mutations as well as predict future possible vulnerabilities.

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As always, this, however, comes with the challenge, and in genomics, they are the following:

  • Extracting the location and structure of genes
  • Identifying regulatory elements
  • Identifying non-coding RNA genes
  • Gene function prediction
  • RNA secondary structure prediction

So will AI be able to overcome those challenges? Let us try to get some insight on genomics first.

Background

What exactly happens when you research a DNA? This means that scientists gain an ability to basically “read” the genetic blueprint, that orchestrates all the activities of the organism. Being a bit more specific, the central postulate of biology can be summarized as a path from DNA to RNA (Ribonucleic acid) to Protein.

DNA is organized in chromosomes, and humans have a total of twenty-three pairs of them. These chromosomes are further organized into DNA segments, called genes, the total sum of which gives you the organism’s genome. Humans possess somewhere around 20,000 genes and three billion pairs and only two percent of those genomes encode a protein, which became the key area of research and business in genomics.

Gene editing and genome sequencing

Since our life cycle, as well as personal diseases and vulnerabilities,  are heavily determined by our genetics, understanding humans’ genetic makeup has become one of the bigger focuses in the sector. But the amount and the complexity of all the data that needs to be evaluated has stalled this process for years. It is only with the help of modern artificial technology programs, as well as machine learning applications we can finally move this rock from the dead spot. Nowadays researchers can interpret and act on genomic data through genomic sequencing and gene editing.

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Gene editing is a method of making specific adjustments to DNA at the organism or cellular levels. Notable gene editing technologies include CRISPR, which offers a cheaper and more effective way of conducting gene editing. Advanced machine learning of the program helps researchers to appropriately select a target sequence, which is one of the most important and challenging 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. Algorithms they use are based on patterns identified in large genetic data sets.
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These sets are later getting translated straight to computer models to help clients understand how these genetic variations influence their cellular processes, like DNA repair, metabolism, and even hair growth. And any negative impact to the normally functioning pathways can lead to different complex diseases.

Future applications

As you can see, AI and machine learning are already making a huge impact on the field and as a result, on our very own lives. This is why it is so important to invest in this matter since it can help in creating many new valuable programs. Future applications of these technologies can potentially contribute to patients personal treatment, create advanced genetic screening tools for newborns and even the potential of gene editing. The possibilities are countless, but we want to highlight the few ones, which in our minds will become the successful applications of the near future:

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Agriculture will be a clear winner. Future technologies can help farmers to crop yield and soil quality. Companies like Trace Genomics are already shifting their interest towards the soil heat. All of these innovations could ramp up the global improvements in the sector that have resulted from past genetic alterations.

A natural progression of precision medicine, pharmacogenomics is an emerging field that is looking on the field of genetics in the context of how one concrete individual responds to drugs. Even though recent, the field shows a solid evidence of research involving machine learning. For instance, what is regarded as the first study of applying machine learning models to determine a stable dose of Tacrolimus in renal transplant patients was first published in February 2017. Tacrolimus is commonly administered to patients, who are following a solid organ transplantation to prevent “acute rejection” of the newly transplanted organ.

Final thoughts

The promise of machine learning for genomics is enormous if it could be fulfilled to the fullest. It could mean near perfect diagnoses, optimized medication and treatment choices, identified high-risk patients, accurately predicted readmission and general empowerment of the personalization of medicine whilst all the time seeing total costs minimized. This is precisely the future we all want and waiting for.

So, what do you think about this? Simply share all your views and thoughts in the comment section below. For the latest tech,social media news and reviews, follow firebebble.com on Twitter, Facebook and Instagram

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