Tolulope Adeyina: Using AI to decode disease, defend against global pandemics

Tolulope Adeyina is a renowned leader in data science, focusing on the application of machine learning and artificial intelligence (AI) to life sciences. Currently pursuing his PhD in data science at the University of Texas at El Paso (UTEP), Adeyina is conducting groundbreaking research that harnesses artificial intelligence, natural language processing, and bioinformatics to address three critical pillars of modern healthcare innovation: early disease detection, drug discovery, and biothreat mitigation.

At the core of Adeyina’s research is the development of advanced machine learning models for biomarker discovery. Biomarkers—biological molecules that indicate the presence or progression of a disease—play a crucial role in early diagnosis and personalised treatment. However, detecting meaningful biomarkers from high-dimensional genomic, transcriptomic, and proteomic datasets remains a significant challenge in medical research. To address this, Adeyina has developed novel AI pipelines that integrate deep learning with statistical genomics to process multi-omic data and identify subtle biological signals. His use of artificial intelligence (transformer models) has enabled the mapping of complex relationships between genes, proteins, and disease pathways, revealing new diagnostic markers that traditional methods often overlook.

These models have demonstrated practical utility in identifying early indicators for chronic and life-threatening diseases, including cancer, autoimmune disorders, and metabolic syndromes. By enabling faster and more accurate diagnoses, Adeyina’s research supports the global push towards precision medicine—a paradigm shift from one-size-fits-all treatments to individualised healthcare strategies.

In addition to diagnostics, Adeyina’s work addresses one of the most pressing needs in pharmaceutical science: the identification of novel protein functions. Using a combination of topic models, unsupervised learning, and knowledge graphs, he is constructing predictive frameworks to infer the functions of under-characterised or newly discovered proteins within cellular systems. These predictions are not only validated against known biological pathways but are also being integrated into ongoing collaborations for drug target identification. His AI-powered systems accelerate the traditional wet-lab process of functional protein analysis, allowing pharmaceutical researchers to prioritise viable drug targets more efficiently, reducing both cost and time in the drug development pipeline.

In the wake of global health crises such as COVID-19 and Lassa fever, Adeyina’s research has gained additional relevance through its contributions to biothreat surveillance and vaccine design. He has developed predictive algorithms capable of analysing the structure, replication patterns, and mutation signatures of RNA viruses. This research has implications for:

• Identifying viral gene targets for vaccine development

• Monitoring evolutionary changes in pathogens for early outbreak detection

• Supporting national biosecurity efforts and global health responses

By integrating state-of-the-art (SOTA) transformer modelling with epidemiological data, Adeyina is helping public health agencies and researchers design early-warning systems for infectious disease outbreaks—an invaluable tool in preventing future pandemics. He is also developing AI-powered products to streamline hospital operations and enhance patients’ healthcare experiences.

Adeyina’s academic background is uniquely suited to his current research. He holds a bachelor’s degree in statistics, a master’s in biometry from the University of Ibadan, and a master’s in bioinformatics from UTEP. His expertise across statistical modelling, molecular biology, and AI engineering allows him to tackle biomedical problems with a rare combination of depth and breadth. He has collaborated with leading institutions, including Texas Tech University Health Sciences Center (TTUHSC) and Pacific Northwest National Laboratory (PNNL), and has presented his findings at top-tier scientific conferences, such as Intelligent Systems for Molecular Biology (ISMB 2024) and Society for Industrial and Applied Mathematics (SIAM MDS 2024).

His work has been featured in peer-reviewed publications and has contributed to funded research projects under the National Institutes of Health (NIH) and other major grant bodies. Adeyina is also a National Teaching Fellow in Data Science—a fellowship sponsored by the Research Centres in Minority Institutions (RCMI) Coordinating Centre, which is supported by the National Institute on Minority Health and Health Disparities (NIH).

Adeyina’s research has earned him recognition as one of the most promising scholars at the intersection of artificial intelligence and the life sciences. His peers and mentors describe him as “an innovator with a sharp instinct for solving real-world biological problems using technology.”

His ability to bridge technical machine learning expertise with life-saving healthcare applications has placed him at the forefront of interdisciplinary scientific advancement. As a proud Nigerian, his contributions serve as an inspiration to the African scientific diaspora and young researchers seeking to impact global health through data science innovation. His work not only advances science but also reinforces the role of African scholars in shaping the future of medicine.

Tolulope Adeyina’s PhD research is more than an academic pursuit—it is a transformative mission to equip the global healthcare system with intelligent, scalable tools for improved diagnostics, targeted treatments, and pandemic preparedness. His extraordinary ability as a data scientist, paired with a profound understanding of biological complexity, marks him as a leader in his field.