Aijalon Shantavia Bettis is a senior double majoring in chemistry and criminal justice with minors in Spanish and finance. Bayesian Deep Learning Approach for the Inference of Genetic Regulatory Networks
Kendall Lemons Mentor: Indika Rathnathungalage Department of Mathematics
Introduction: The major objective of this project is to construct a novel computational framework. Successfully modeling genetic regulatory networks can lead to the targeting of specific genes underlying complex diseases. Additionally, the intersection of statistics and genetics is a growing field, the development of these models is increasing, and most are cell/tissue-specific. Lastly, most models focus on specific mutations that lead to a particular disease. We aim to take a holistic approach, looking at all possible pathways that lead to disease and disorders. Materials and Methods: We use Artificial Neural Network (ANN) and Deep Neural Networks in this study. These procedures are implemented using Python/R language. Results and Discussion: At this stage, we have completed literature about the underline problem. In addition, skills for programming in Python/R were improved to construct the Neural Network. Conclusion(s) or Summary: According to the information gathered so far, the Bayesian approach for the Deep Neural Network is advantageous than the Deep Neural Network. It is expected to prove this experimentally in the future.
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Awardee and Student:
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Dr. Indika Rathnathungalage is an Assistant Professor with research interests in Statistics, Data Analytics, and Machine Learning. Kendall Lemons is a senior majoring in Mathematics & Mechanical Engineering
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