ORCID ID

kshah2@uno.edu

Date of Award

12-2022

Degree Type

Thesis-Restricted

Degree Name

M.S.

Degree Program

Computer Science

Department

Computer Science

Major Professor

Md Tamjidul Hoque

Abstract

Noncoding RNAs (ncRNAs) play a significant role in several fundamental biological processes by binding to RNA-binding proteins (RBPs); hence, it is necessary to study ncRNA-protein interaction (RPI). Several classic and deep-learning machine learning models have been pro-posed to predict RPI. These models first need to collect features of RNA and protein, such as physicochemical properties, secondary and tertiary structure, et cetera, before feeding them into the model. More recently, after the advancement of high throughput sequenc-ing and the improvement in Natural Language Processing (NLP), transformer models like BERT-RBP and Evolutionary Scaling Model (ESM) can be trained to automatically extract feature representations, containing both low and high-level information, from RNA and pro-tein sequences directly. This method could make manual feature collection optional. Hence, in this study, we compare the performance of such language-based features against manually created features to predict the interaction probability between a protein and an RNA.

Rights

The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.

Available for download on Saturday, December 16, 2023

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