A genome is an organism's complete set of deoxyribonucleic acid (DNA), a chemical compound that contains the genetic instructions needed to develop and direct the activities of every organism. The DNA molecules are made of two strands and each strand is made of four chemical units. The bases are adenine (A), thymine (T), guanine (G) and cytosine (C). Bases on opposite strands pair specifically; an A always pairs with a T, and a C always with a G.
Artificial Intelligence (AI) is the ability of a digital computer or computer-controlled robot to perform tasks which are normally associated with human beings. AI is a science and a set of computational technologies that are inspired by but typically operate quite differently from the ways people use their senses to learn, reason, and take decisions and actions. AI can be created as software or tools. They are capable of imitating human intelligence in certain instances and sometimes they can even exceed human potential.
Sequencing refers to determining the exact order of the base pairs in a segment of DNA. The primary method used by the Human Genome Project (HGP) to produce the finished version of the human genetic code was map-based, or bacterial artificial chromosome (BAC) based sequencing. Human DNA is disintegrated into pieces that are manageable in size. The fragments are then cloned in bacteria, which store and replicate the human DNA so that it can be prepared in quantities large enough for sequencing.
4. Molecular Assemblies
6. WuXi App Tech
In the field of Genomics, the expectations for Artificial Intelligence are very high. It is assumed that in Genomic medicine and research, AI, machine learning and deep learning are in the ascendancy. Some examples of AI applications in the genomics space include drug discovery, gene editing, and variant analysis. There are also a growing number of academic machine learning resources for genomics, some of which have already been routinely used in clinical genomics analysis for some time. So, this is evidential of how great genomics and AI work effectively together and are thus, a good match. Coupled with more powerful computing infrastructure, machine learning and deep learning are presenting diverse opportunities. By facilitating the analysis of large and complex research datasets, machine learning will accelerate new discoveries in genomic medicine: current studies are seeking to understand how cancers evolve, to examine microbiomes, and to analyze multi-omics datasets. The need to facilitate the safe and effective deployment of AI for genomic medicine, and other areas of healthcare. Failure to act promptly would risk:
1. Compounding existing disparities. As AI is applied more routinely to genomic datasets, some pre-existing challenges will be further deepened, notably the lack of diversity in genomic datasets and databases. An imbalance of information on some populations can lead to misdiagnosis, as well as uneven success rates in personalized medicine and clinical trial outcomes. If left unresolved, the development of AI algorithms using unrepresentative genomic datasets will perpetuate and further entrench health disparities for underserved groups.
2. Opportunity costs. A significant amount of investment is being poured into growing AI for healthcare. To make the most of this investment, it is crucial for AI to be channeled effectively to address the most pressing problems together with those where AI is most likely to add value. This requires close collaboration between AI practitioners and genomics domain experts to identify the most appropriate questions to address, determine which machine learning approaches to apply, and to recognise limitations in datasets, methods, and current knowledge so as to avoid AI models that may lead to misleading insights or faulty predictions.
3. Over-reliance on technology to solve complex problems. Despite its vast potential, AI alone will not advance genomic medicine and it certainly cannot do so without the necessary oversight, safeguards, validation, robust ethical appraisals, and public engagement. The temptation for 'tech solutionism' has come into sharp focus during the current pandemic, and recent reports and commentaries have warned against the rushed deployment of AI and digital technologies without credible supporting evidence and careful oversight.
Examples of technologies (AI) that are transforming the medical field include: high-throughput genome sequencing, CRISPR, and single-cell genomics.
Even though using AI tools in genomics is still in its prime stages, researchers have benefitted immensely from developing programs that are able to assist it in specific ways. Examples of this include the following:
1. Examining people’s faces with facial analysis AI programs to accurately identify genetic disorders.
2. Using machine learning techniques to identify the primary kind of cancer from a liquid biopsy.
3. Predicting how a certain kind of cancer will progress in a patient.
4. Identifying disease-causing genomic variants compared to benign variants using machine learning.
5. Using deep learning to improve the function of gene editing tools such as Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR). CRISPR provides the power to edit. E.g. Correct typos, or “mutations,” that can arise in genomes — and to do so with an unprecedented level of precision.
There remains an increase in complexity and number of DNA sequencing and techniques and as such, there is the need for Artificial Intelligence or Machine Learning in Genomics. Genomics researchers rely on AI computational tools that are robust enough to manage and interpret any valuable information that might be hidden in any large dataset.
DNA sequencing and other biological techniques will continue to increase the number and complexity of such data sets. This is why genomics researchers need AI/ML-based computational tools that can handle, extract and interpret the valuable information hidden within this large trove of data.
1. The genomics field continues to expand the use of computational methods such as artificial intelligence. In doing so, it helps to improve our understanding of hidden patterns in large and complex genomics data sets from basic and clinical research projects.
2. AI, specifically, Machine learning analysis is beneficial for disease research and genomic tools like CRISPR.
3. National Human Genome Research Institute (NHGRI) is identifying and shaping its unique role in the convergence of genomic and machine learning research.
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A sensor or instrument may need to undergo a series of changes known as sensor calibration in order for the instrument to operate as correctly and error-free as feasible. The benefits of calibrating include some of the following.
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