Hayrettin Okut

University of Kansas United States of America

Hayrettin Okut, PhD, is a Research Professor at the University of Kansas School of Medicine–Wichita. He earned his doctorate in Biostatistics/Biometry in 1992 and previously served as a Senior Research Associate at Wake Forest University’s Center for Genomics and Personalized Medicine. His career includes extensive teaching and collaborations with institutions such as the University of Wisconsin–Madison, Oregon Research Institute, and the University of Oregon. Dr. Okut completed postdoctoral research at the University of Nebraska as a TÜBİTAK scholar and at the University of Oregon as a Fulbright Scholar. Since 2011, he has pioneered the application of Artificial Neural Networks in genomic prediction, publishing numerous peer-reviewed papers and book chapters, and presenting widely at international conferences.

1books edited

3chapters authored

Latest work with IntechOpen by Hayrettin Okut

In an era where precision medicine and individualized healthcare are revolutionizing clinical practice, comprehending genetic variations has become increasingly important for enhancing human health. This authoritative volume examines the advances in methods for unraveling the causalities of genetic variations and cytogenetics, providing readers with a detailed analysis of powerful analytical methods that assess causal relationships, and how changes in DNA sequence, ranging from single-nucleotide polymorphisms to intricate chromosomal rearrangements, affect susceptibility to disease, responses to therapy, and population health outcomes. The book conjoins fundamental genetic concepts with methodological breakthroughs, including Mendelian randomization, next-generation sequencing, polygenic risk scoring, and high-performance computational genomics. The reader will discover how new approaches are revolutionizing the diagnosis of microdeletion syndrome, enabling accurate genotype-phenotype correlations that guide clinical decision-making. The book integrates concepts from population genetics and evolutionary biology in a unique way to explain how natural selection and migration gradients have shaped current genetic diversity and disease resistance mechanisms. Special emphasis is placed on emerging methodologies, such as Environmental Pollutant-based Polygenic Risk Scores and Mendelian randomization approaches, that develop novel paradigms in gene-environment interactions. From antibiotic resistance mechanisms to cancer susceptibility risk factors, the book provides insightful critical examinations of how genetic research is addressing contemporary public health challenges. The convergence of artificial intelligence and machine learning technologies is a sign of the future direction of gene research and the identification of therapeutic targets. This book is essential for researchers, clinicians, and graduate students who want to understand the complex relationship between genetic variation and human disease. It highlights ethical considerations and the appropriate use of genetic medicine within diverse, global populations.

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Unraveling the Causalities of Genetic Variations

Edited by Hayrettin Okut

Chapters authored

Deep Neural Networks and Applications in Medical Research

By Serdar Abut, Hayrettin Okut, Rosey Zackula and Ken James Kallail

Artificial Intelligence (AI) has played a significant role in improving decision-making within the healthcare system. AI includes machine learning, which encompasses a subset called artificial neural networks (ANNs). These networks mimic how biological neurons in the brain signal one another. In this chapter, we conduct a seminal review of ANNs and explain how prediction and classification tasks can be conducted in the field of medicine. Basic information is provided showing how neural networks solve the problem of determining disease subsets by analyzing huge amounts of structured and unstructured patient data. We also provide information on the application of conventional ANNs and deep convolutional neural networks (DCNNs) that are specific to medical image processing. For example, DCNNs can be used to detect the edges of an item within an image. The acquired knowledge can then be transferred so that similar edges can be identified on another image. This chapter is unique; it is specifically aimed at medical professionals who are interested in artificial intelligence. Because we will demonstrate the application in a straightforward manner, researchers from other technical fields will also benefit.

Part of the book: Deep Learning

Discovering Causality with Mendelian Randomization: What It Is and How to Do It

By Hayrettin Okut and Rosey Zackula

Mendelian Randomization (MR) is a powerful analytical method that assesses causal relationships. When direct exposure is neither feasible nor ethical, this method incorporates an instrumental variable (such as a genetic variant) as a substitute for an exposure, and measures the causative influence directly related to an outcome (e.g., disease). In this way, MR eliminates the need for an unattainable randomized experiment. However, the instrumental variable must meet three core assumptions: be associated with the exposure of interest, unrelated to any confounding factors that impacts the outcome, and affect the outcome only through the exposure. Conducting an MR study requires six basic steps: (1) define the causal association, (2) select an instrumental variable, (3) evaluate core assumptions, (4) conduct an analysis that produces a model, (5) validate the model, and (6) report findings. Various software offers MR analyses: R, Python, SAS, STATA, and other specialized programs. Although MR is a powerful tool, it has challenges and limitations. For example, instrumental variables can influence multiple traits (pleiotropy), be weak instruments, or can be underpowered. Future advancements aim to correct pleiotropy and expand the range of instrumental variables including genetic variants. MR represents a significant advancement in causal inference in medicine; it overcomes the limitations imposed on randomized clinical trials and is superior to observational studies that only measure associations. This chapter will outline current MR methodologies and provide a framework for those interested in investigating causal relationships using genetic variants.

Part of the book: Unraveling the Causalities of Genetic Variations

Introductory Chapter: The Evolving Landscape of Genetic Variation Research

By Hayrettin Okut

Part of the book: Unraveling the Causalities of Genetic Variations

Hayrettin Okut

Latest work with IntechOpen by Hayrettin Okut

Unraveling the Causalities of Genetic Variations

Chapters authored

Related collaborators

Jaydip Sen

Robertas Damaševičius

Serdar Abut

Ken James Kallail

Vinayak Bharadi

Suha Mukadam

Rahul Prasad

Kavyashree Upparakakula

Janhavi Jaygade

Rosey Zackula

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