IR-ER- A Hybrid Pipeline for Classifying COVID-19 RNA Seq Data
Authors: 
Girija Rani Karetla, Daniel Catchpoole, Paul Kennedy, Simeon Simoff, Quang Vinh NguyenAuthors Info & Claims
Pages 183 - 189
Abstract
Bioinformatics has numerous approaches for evaluating the similarities between RNA-seq data for disease classification. Processing RNA-sequencing (RNA-seq) data using clustering or classification approach is extremely challenging, although analysis of ribonucleic acid (RNA-Seq) helps understand differentially expressed genes and classify the patient in a risk-free method. In this study, we present a hybrid end-to-end pipeline for analyzing, processing, and classifying the RNA-Seq data with a major focus on the covid-19 data set. The pipeline has been developed in three phases initially the raw data is normalized. Then the normalized data is pushed to a colonization algorithm to remove the noise data. The optimized data set is passed to a Deep Learning (DL) classifier. Further, a comparative analysis is performed with state of art methods discussed in the literature. The results prove that our proposed hybrid pipeline achieved the best accuracy over other methods. Gene set enrichment analysis was also performed to analyze the genes that are informative towards COVID-19 identification.
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Published In
January 2023
272 pages
ISBN:9798400700057
DOI:10.1145/3579375
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Published: 13 March 2023
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ACSW 2023
ACSW 2023: 2023 Australasian Computer Science Week
January 30 - February 3, 2023
VIC, Melbourne, Australia
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Overall Acceptance Rate 61 of 141 submissions, 43%
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