Date of Graduation

5-2023

Document Type

Thesis

Degree Name

Bachelor of Science in Industrial Engineering

Degree Level

Undergraduate

Department

Industrial Engineering

Advisor/Mentor

Rainwater, Chase E.

Committee Member/Reader

Kent, John L.

Abstract

Machine learning is a field with high growth potential due to the overall continuous progressions, developments, advancements, and improvements caused by the way it is used to help interpret and use large amounts of data [1]. One type of data that can be collected and analyzed by these machine learning models is data that is associated with DNA and information that the DNA gives. The research will be focusing specifically on using machine learning technology to detect pathobiomes indicative of salmonella pork. The pathobiome associated with salmonella is very similar to others, and this causes a problem for classification/detection with short-read sequencing platforms [2]. Because of this, it is important for decision makers to understand what kind of data is needed to help accurately predict these pathobiomes. This research consists of a variety of experiments that help determine what this kind of data is. This is done by reading data taken from various sequences from The National Center for Biotechnology Information (NCBI) database. This project is also being conducted within the backdrop of an existing project from the Walmart foundation project, Improving Food Safety of Pork Supply Chain [3].

[1] B. K, “5 essential steps for every deep learning model! — by bharath k — towards data science,” https://towardsdatascience. com/5-essential-steps-for-every-deep-learning-model-30f0af3ccc37, November 2022, (Accessed on 03/04/2023).

[2] C. J. Grim, N. Daquigan, T. S. Lusk Pfefer, A. R. Ottesen, J. R. White, and K. G. Jarvis, “High-resolution microbiome profiling for detection and tracking of salmonella enterica,” Frontiers in microbiology, vol. 8, p. 1587, 2017.

[3] C. E. Rainwater, “Improving food safety of pork supply chain, walmart foundation,” 2021.

Keywords

Machine Learning; DNA; Pathobiome

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