Artificial Intelligence in the Genomics Era: A Blessing or a Curse?

As it offers tremendous potential for genomic advancements and has become an invaluable tool in understanding and harnessing the power of genetic information, artificial intelligence has revolutionised genomics by enabling researchers to analyse complex genomic data, unravel biological insights, and accelerate discoveries in personalised medicine, disease diagnosis, drug development, and gene editing. Artificial intelligence can be considered both a boon and a bane in the field of genomics, depending on how it is applied and its implications. Artificial intelligence has the potential to bring significant benefits to genomics, including improved data analysis, precision medicine, and drug discovery. However, ethical considerations, interpretability challenges, biases, and the need for human expertise must be carefully addressed to fully harness the potential of artificial intelligence in genomics and ensure its responsible and beneficial application. The purpose of this narrative review is to shed light on the various applications of genomics that take advantage of technological advancements in artificial intelligence, and to identify its benefits and limitations.


Introduction
Genomics is the study of an organism's genetic information, including DNA sequencing, gene expression and genetic variation.It has made enormous significant strides and has become an integral part of medicine and scientific research.Precision medicine, which tailors' medical treatment to an individual's genetic makeup by analysing a person's genome, understanding the susceptibility to certain diseases, and predicting the response to specific treatments, allows for the development of personalized therapeutic approaches.Among its many applications are cancer genomics, genetic testing and screening, infectious disease surveillance, gene editing technologies, applications in bioinformatics, epigenomics, and more recently, single cell sequencing genomics.Artificial Intelligence (AI) has played an increasingly important role in genomics research in recent years.AI has proven to be a powerful tool in analysing and interpreting large-scale genomic data, and has contributed significantly to our understanding of genetic disease, drug development, and personalized medicine.

Methodology
This narrative review was developed using a BOOLEAN search with the terms 'artificial intelligence' AND 'genomics' OR 'proteomics' in the search engines PubMed, Scopus, Web of Science and Connected papers as on May 2023.As this is only a brief narrative review, only the most pertinent literature that was scrutinized and selected by the authors was included, and no specific inclusion or exclusion criteria were applied.

Genome sequencing
AI can be used to analyze vast amounts of genomic data, enabling researchers to identify genetic variations and mutations that may be associated with disease.AI algorithms can also be used to predict the function of genes and proteins, aiding in drug development [1].

Disease diagnosis
AI can help identify patterns and correlations in genomic data that may be associated with disease.For example, AI can be used to identify genetic mutations that are more common in patients with a specific type of cancer [2].

Personalized medicine
The genetic profiles of individual patients can be used in conjunction with artificial intelligence to better personalize treatment plans.This can lead to treatments that are more effective and tailored, reducing the likelihood of unwanted side effects [3,4].

Drug development
AI can help identify potential drug targets by predicting the function of genes and proteins.This can speed up the drug development process and reduce the cost of bringing new drugs to market [5].AI is already playing a significant role in the field of genomics and it is expected to continue to grow and advance in the future.
The ability of AI to analyse and interpret large-scale genomic data is particularly valuable, as the amount of genetic data generated by sequencing technologies is increasing rapidly.AI has the potential to identify genetic mutations and patterns that are difficult for humans to detect, leading to new insights into genetic disease and personalized medicine.Moreover, AI has the potential to accelerate drug discovery by identifying new drug targets and predicting the efficacy of potential therapies.It can also aid in the development of new diagnostic tools, which can help with early detection and treatment of genetic diseases [6].In addition, AI can assist in the integration of multiple sources of data, such as electronic health records, genomic data, and environmental data, providing a more comprehensive view of an individual's health.Therefore, AI is likely to play an increasingly important role in genomics research and applications in the future, facilitating new discoveries and improving patient outcomes [7].However, it is important to note that while AI can provide valuable insights, it should be used in conjunction with human expertise and ethical considerations to ensure the responsible and effective use of this technology.

Methodologies of AI in genomic research
Machine learning is a type of AI that involves training algorithms to recognize patterns in data.In genomics, machine learning algorithms can be used to identify genetic mutations and patterns that are associated with disease.This can help researchers develop new diagnostic tools and therapies [8].Deep learning is another type of machine learning that uses neural networks to analyse and interpret data.Deep learning algorithms can be used to predict the function of genes and proteins, as well as model their interactions with other molecules [9].Natural language processing (NLP) is a type of AI that is used to analyse and interpret human language.In genomics, NLP can be used to extract relevant information from scientific literature and electronic health records [10].Image analysis in AI methodology is commonly used in medical imaging to analyse microarray and sequencing data, as well as identify genetic markers associated with disease [11].Bayesian networks are a type of probabilistic graphical model that can be used to represent and analyse complex systems.In genomics, Bayesian networks can be used to model gene expression and gene regulation [12].
Overall, these AI methodologies are used in a variety of ways in genomics research, from identifying genetic mutations associated with disease to developing new drug targets and therapies.It is important to note that AI is not a replacement for laboratory machines in genomic research, but rather a complementary technology that can enhance and accelerate the research process.Laboratory machines, such as sequencers and microarrays, are essential tools for generating genomic data, while AI algorithms can be used to analyse and interpret that data.
In fact, many applications of AI in genomics require the use of laboratory machines to generate data, such as DNA sequencing data or microarray data.AI algorithms can then be used to analyse this data and identify patterns, mutations, and other genetic markers that could be associated with disease [13].Moreover, laboratory machines are essential for validating the results obtained from AI analysis.For example, if AI algorithms predict a certain genetic mutation to be associated with a particular disease, this prediction needs to be validated experimentally using laboratory machines, such as Polymerase Chain Reaction or Sanger sequencing [14].Therefore, while AI has great potential in genomics research, it is not a replacement for laboratory machines, but rather a complementary technology that can enhance and accelerate the research process.AI has the potential to revolutionize the field of genomics by enabling faster and more efficient analysis of genomic data, which can lead to new insights into genetic disease, improved drug development, and more personalized healthcare.In the future, AI is expected to play an even greater role in genomics research.AI algorithms can help tailor treatments to individual patients based on their genetic profile (Figure 1).This can lead to more effective and targeted treatments that minimize side effects.AI algorithms can be used to identify potential drug targets and develop new therapies based on genomic data.AI can be used to help predict the potential effects of genomic editing techniques, such as CRISPR-Cas9, and improve the precision and accuracy [15].AI algorithms can be used to develop new diagnostic tools for genetic diseases, enabling earlier and more accurate diagnosis.AI can help integrate genomic data with other types of data, such as electronic health records and environmental data, providing a more comprehensive view of an individual's health.
Overall, the future of AI in genomics research looks promising, and it has the potential to significantly improve the understanding of genetic disease and improve healthcare outcomes [16].

Advantages of AI in genomics
AI algorithms can handle large and complex genomic data sets that would be difficult for humans to analyse manually.This enables researchers to identify patterns, mutations, and other genetic markers that could be associated with disease.AI algorithms can be used to predict the function of genes and proteins, as well as model their interactions with other molecules.This can help researchers develop new drug targets and therapies [17].AI can help tailor treatments to individual patients based on the genetic profile.This can lead to more effective and targeted treatments that minimize side effects.AI can automate repetitive and time-consuming tasks, such as data analysis and quality control, leading to faster and more efficient genomics research.AI algorithms can provide more accurate and reproducible results than manual analysis, reducing the potential for human error [18].AI can help integrate genomic data with other types of data, such as electronic health records and environmental data, providing a more comprehensive view of an individual's health.Overall, AI has the potential to revolutionize genomics research, leading to new insights into genetic disease, improved drug development, and more personalized healthcare [19].

Limitations of AI-based tools in genomics
While AI has great potential in the field of genomics, there are also some limitations that need to be considered.Therefore, the accuracy and reliability of the genomic data used to train AI algorithms is critical.Inaccurate or incomplete data can lead to misleading results.AI algorithms can generate complex and sometimes difficult-tointerpret results [20].While AI can enhance genomic analysis and decision-making, it should not replace human expertise.Clinical decisions based on genomic data should involve a combination of AI-driven insights and the expertise of healthcare professionals.It is crucial to maintain a balance and ensure that AI is used as a tool to augment human capabilities rather than replace them.Therefore, it is important to have experienced and knowledgeable professionals who can interpret the results and make informed decisions based on them.AI algorithms can sometimes become overfit to the training data, leading to poor performance on new data.This can be particularly problematic in genomics research, where there is a high degree of genetic variation [21].The use of AI in genomics raises ethical concerns, including privacy and data security.Genomic data is highly sensitive and can reveal sensitive information about individuals and the families.It is crucial to ensure proper data protection and privacy regulations are in place to prevent unauthorized access or misuse of genomic information.AI algorithms can be computationally expensive and require significant resources, including high-performance computing and large data storage capabilities [22].AI models are trained on existing datasets, which may contain biases and disparities.If these biases are not properly addressed, AI algorithms may perpetuate existing inequalities in healthcare [23].It is important to ensure diverse and representative datasets and address potential biases in algorithm development to prevent exacerbating healthcare disparities.

Way forward of AI-based genomic advances
AI was already playing a significant role in genomic research, and it's likely that its importance has only grown since then.AI's potential in genomic research lies in its ability to analyse vast amounts of genomic data quickly and accurately, providing valuable insights that could lead to breakthroughs in various areas of medicine and biology.The future applications are going to be led to more personalized and effective medical interventions tailored to person's unique genetic profile even before the detection of a disease or condition [24].This can also provide a valuable and promising approach for characterizing genetic variants leading proper health risk assessment to unravel these complex interactions and provide a deeper understanding of disease mechanisms.The future of artificial intelligence in genomic research is undoubtedly exciting, and it has the potential to transform healthcare and our understanding of genetics profoundly.Continued research, collaboration between experts in genomics and AI, and ongoing technological advancements will be crucial to fully unlock the potential of AI in this field [25].

Conclusion
Artificial intelligence has a lot of untapped potential in the field of genomics research; nevertheless, it is essential to keep these limitations in mind and make sure that the technology is used in a responsible and efficient way.In conclusion, artificial intelligence has already made important contributions to the field of genomics by making it possible to analyse genomic data in a quicker and more effective manner.The use of AI algorithms allows for the identification of genetic mutations, the prediction of the function of genes and proteins, as well as the development of new diagnostic tools and treatments.Overall, genomics has had and will continue to have far-reaching, gamechanging effects across a wide range of scientific fields.Current research and technology developments are expanding the topic and its potential uses and benefits to

Figure 1 :
Figure 1: AI algorithms can help tailor treatments to individual patients based on their genetic profile.