Artificial Intelligence: The New Age of Biobanking and Medical Research

Artificial Intelligence: The New Age of Biobanking

Artificial intelligence (AI) is slowly but surely transforming the practice of medicine. Advancements in digital data collection, computing infrastructure, and machine learning have allowed artificial intelligence applications to spread into previously untapped domains. Today, the revolution in biobanks, biorepositories, and biospecimen science is being fueled by AI-driven process automation, the internet, robots, and other quickly technical developments that are helpful in modern biobanking. With the expansion of biobanking from a basic frozen specimens’ collection to the virtual biobanks that exist today, the advancements in biobanking is leading to confer revolutionary potential on healthcare systems.1

Biobanks are quickly evolving and allowing for the collection of enormous amounts of human and non-human biological material and their related information for application in medical research and drug development. Research into personalized medicine and other new areas might be aided by the proliferation of diverse biobanks and data-sharing capabilities.

Many questions regarding the impact of genetic variation on human health can be answered by integrating genomic data with electronic health record data. These problems can be answered with the help of artificial intelligence and machine learning. Computing technologies are well-suited to the task of developing phenotypes that are more precise than those supplied by billing codes for diseases.2

The fundamental objective of that technology is to create a mathematical function that can be utilized to generate a more accurate diagnosis using laboratory measurements, medicine, and other information. Using machine learning, it is possible to create disease phenotypic models.

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Since the advent of genome-wide investigations in epidemiological research, statistical testing of each genetic variation has been largely ignored. This provides advantages, such as speed and ease of use. This may not be the case with genetic variations, which may have context-dependent consequences. The non-additive effects of numerous elements can be modeled using artificial intelligence.

The genetic heterogeneity that might be relatively prevalent can be traced or captured by machine learning as well. The field of artificial intelligence in biobanks is still in its infancy, but there is a lot of interest in it. Issues like selecting the best machine learning algorithms for the dataset and translating the findings into meaningful implementation strategies still need more investigation.3

Automated machine learning is a new innovative field that is helpful to solve data-related issues by focusing on algorithms for selecting the best approaches for a given data set. Automated machine learning aims to design algorithms that solve problems in the same manner that human analysts do. Remember that the purpose of machine learning is to uncover those outcomes that would otherwise go unnoticed by traditional statistical approaches.4

In near future, artificial intelligence technology will define and help to quantify the quality of biosamples; for example, the AI system will be able to detect the integrity of DNA using pictures of DNA gel electrophoresis and the proportion of malignancy and necrosis in tissue samples using digital histopathology images. The findings of short tandem repeat (STR) analysis and single nucleotide polymorphism (SNP) genotyping used to ensure the quality of biosamples and that can be used to determine whether or not they match the participants’ gender information or DNA sequencing data.

Additionally, artificial intelligence will develop a strategy for future biomedical research by studying the biobanks distribution and inventory status and research trends. Due to the fact that biosamples are employed in research, vacant space in biosamples storage equipment occurs infrequently. If an AI system is connected to an automated sample storage system, it will re-locate biosamples to maximize storage space utilization.5

Even though everyone knows that the implementation of artificial intelligence is ongoing in various scientific fields, including healthcare and medical research. Due to this progress in medical science, we hopefully anticipate that a new generation of biobanking will be introduced in the near future that will be run on AI to digitalize big data with great advancements. Large volumes of data can be processed concurrently and quickly by AI systems, and each incremental example may be learned from to increase accuracy constantly. Artificial Intelligence may be used in a variety of ways in the medical industry, including disease or cancer diagnosis and prognosis.6

In the context of AI, precision medicine is an emerging model that will benefit significantly from advances in artificial intelligence, machine learning, deep learning, and big data analytics. Precision medicine is a new approach for clinical treatment that uses the complex interplay between an individual’s genetic makeup, way of life, behavior, and environment. It has enormous potential for the healthcare industry, including drug discovery. In addition, wearable sensors and information and communication technology, in general, are helping to improve human healthcare.

Furthermore, precision medicine aims to identify an individual’s specific risk of developing a disease and provide individualized approaches to prevention and therapy. The utilization of massive complex datasets that combine individual genes, functions, and environmental differences can improve diagnosis, create treatment approaches, and predict prognosis.7

In the present omics era of big data, the introduction of artificial intelligence or machine learning-based algorithms has transformed the paradigm of one gene, one target, one drug into a framework of non-selective targets, even for a single medicine. For example, in this context, artificial intelligence, machine learning or deep learning algorithms can learn from heterogeneous information, find novel therapeutic targets, repurpose existing ones or finally lead the decision-making procedure. Using worldwide clinical data sharing programs for COVID-19, it was recently proved that artificial intelligence and machine learning may help physicians quickly classify the degree of infection and thus choose the most successful treatment method.8

Moreover, the drug development community can profit from vast amounts of expression data from target tissues or organs by applying artificial intelligence in biobanking. To find cell membrane receptors that regulate gene expression in diseases, this data is useful. This enables medicinal chemists to understand the disease’s mechanism of action, trace back the target, search existing databases for pharmaceuticals with an inhibitor, and computationally forecast the efficacy, potency, and selectivity of different medicines.9

In light of the fact that artificial intelligence in biobanking is going to change the globe and all we know is that AI is rapidly playing a vital role in becoming a primary key area in the medical field and human healthcare development. However, the huge investments in this particular field will be helpful to enable the progress in medical science that will shape somehow any nation’s economic infrastructure by influencing our comprehensive knowledge about human health, personalized medicine, drugs, disease, and many other scientific fields. The time has come to devote more attention to AI-based digital biobanking and better understand its dangers and benefits.10


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