Date of Publication
2023
Document Type
Dissertation/Thesis
Degree Name
Master of Science in Computer Science
College
College of Computer Studies
Department/Unit
Software Technology
Thesis Advisor
Dr. Anish M.S. Shrestha
Defense Panel Chair
Dr. Charibeth Cheng
Defense Panel Member
Dr. Llewelyn Espiritu
Dr. Anish M.S. Shrestha
Abstract (English)
With the rise of antimicrobial resistance that decreases the effectiveness of antibiotics in treating bacterial infections, phage therapy is being studied as an alternative to antibiotics. Phage therapy is the use of phages to treat bacterial infections by letting the phages infect and lyse the bacterial pathogen at the site of infection. Phages are known to be able to infect a narrow range of hosts only, but laboratory experiments to verify an interaction between a phage and a bacterium are both costly and time-consuming. To mitigate this, several studies explored the use of machine learning classifiers to predict whether a phage host interacts or not. In this study, we explored different kinds of protein representation, including protein embeddings that are generated by protein language models, that can serve as an input to machine learning classifiers. Our experiments show that protein embeddings do not necessarily improve classifier performance compared to using the conventional k-mer profile representation.
Abstract Format
html
Language
English
Recommended Citation
Dizon, F. (2023). Performance of various protein representations for predicting phage host interaction. Retrieved from https://animorepository.dlsu.edu.ph/etdm_softtech/10
Upload Full Text
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2023_Dizon_Chapter1.pdf (53 kB)
2023_Dizon_Chapter2.pdf (317 kB)
2023_Dizon_Chapter3.pdf (473 kB)
2023_Dizon_Chapter4.pdf (579 kB)
2023_Dizon_Chapter5.pdf (45 kB)
2023_Dizon_References.pdf (126 kB)
2023_Dizon_AppendixA.pdf (852 kB)
2023_Dizon_AppendixB.pdf (44 kB)
Embargo Period
12-10-2023
