Publication Date
Spring 2018
Degree Type
Master's Project
Degree Name
Master of Science (MS)
Department
Computer Science
Abstract
Mitochondrial Cytochrome C Oxidase subunit I (CO I – to be read as “see – oh one”) is a 658 base pair region in the gene encoding that is proposed as standard barcode for animals. Meaning, the CO I is a special region found in animal DNA that is studied to identify the species of the animal. Currently, there is an implementation of an algorithm called ARBitrator which identifies and extracts these CO I sequences from enormous genes database called GenBank. The ARBitrator is good at extracting the CO I sequences that have better specificity and accuracy as compared to other existing algorithms for CO I sequence identification[1][2]. Now, this project aims at training a neural network to learn the features of the CO I sequences extracted by ARBitrator, so that this neural network can be used in future to further recognize CO I sequences. Effectively, we are aiming to successfully design, train, and use a deep learning neural network to learn to recognize CO I sequences in a supervised way. This is the first time that a neural network is explored and used for this purpose.
Recommended Citation
Marathe, Saurabh, "A Neural Network Classifier for the COI Barcode Gene" (2018). Master's Projects. 604.
DOI: https://doi.org/10.31979/etd.2tmv-y2rq
https://scholarworks.sjsu.edu/etd_projects/604
