2 Emerging Trends | Artificial Intelligence

Suraj Sakaram & Panashe Nyengera 

What breakthroughs can artificial intelligence help computational biologists make to help make personalized medicine personal?

Artificial Intelligence, particularly machine learning, has made many breakthroughs in bioinformatics, aiding in the analysis and interpretation of large biological datasets. Machine learning algorithms, including neural networks and decision trees, are being adapted more efficiently to sift through gigantic biological datasets. Whether it’s predicting protein folding, identifying biomarkers, or classifying subtypes of diseases, machine learning is increasingly indispensable in bioinformatics.

Artificial Intelligence can be applied in protein analysis and interpretation have provided a more detailed and precise understanding of protein folding. Protein folding focuses on analyzing and determining the 3-dimensional shape of proteins which in turn influences their function (Alberts et al., 20020). Machine learning algorithms have successfully unfolded the challenges in understanding protein folding due to the number of possible conformations. The success of machine learning algorithms has led to the correct interpretation of the complex relationships between protein sequences and their folded structures.

Additionally, artificial intelligence has also been adopted in the identification of biomarkers.  Biomarkers measure biological processes, pathogenic processes, and pharmacologic responses to therapeutic intervention (Hirsch et al.,  2020). These biomarkers provide valuable insights into disease diagnosis,  progression, and response to treatment. Machine learning algorithms analyze biomarker data and predict an individual’s response to personalized medicine (Restrepo et al., 2023)

Artificial intelligence can also help classify disease subtypes. Varying machine learning techniques are now adapted to data set sizes. Biomarkers with AI tools can be classified to pinpoint bodily regions of interest to help with diagnostics.  New biomarkers can be classified in a way that leads to more accurate treatment strategies. 

Applying artificial intelligence to protein folding, identifying biomarkers, or classifying disease subtypes will help bioinformatics make personalized medicine personal for you someday soon.


Alberts B, Johnson A, Lewis J, Raff M, Roberts K, Walters P (2002). “The Shape and Structure of Proteins”Molecular Biology of the Cell; Fourth Edition. New York and London: Garland Science. ISBN 978-0-8153-3218-3.

Hirsch MS, Watkins J (May 2020). “A Comprehensive Review of Biomarker Use in the Gynecologic Tract Including Differential Diagnoses and Diagnostic Pitfalls”. Advances in Anatomic Pathology27 (3): 164–192. 

Restrepo, J.C., Dueñas, D., Corredor, Z. and Liscano, Y., 2023. Advances in Genomic Data and Biomarkers: Revolutionizing NSCLC Diagnosis and Treatment. Cancers15(13), p.3474.