IR-ER- A Hybrid Pipeline for Classifying COVID-19 RNA Seq Data

Karetla, GR; Catchpoole, D; Kennedy, P; Simoff, S; Nguyen, QV

IR-ER- A Hybrid Pipeline for Classifying COVID-19 RNA Seq Data

Karetla, GR Catchpoole, D

Kennedy, P

Simoff, S Nguyen, QV

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Publisher:: ASSOC COMPUTING MACHINERY
Publication Type:: Conference Proceeding
Citation:: ACM International Conference Proceeding Series, 2023, pp. 183-189
Issue Date:: 2023-01-30

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Field	Value	Language
dc.contributor.author	Karetla, GR
dc.contributor.author	Catchpoole, D https://orcid.org/0000-0001-5836-1413
dc.contributor.author	Kennedy, P https://orcid.org/0000-0001-7837-3171
dc.contributor.author	Simoff, S
dc.contributor.author	Nguyen, QV
dc.date	2023-01-31
dc.date.accessioned	2024-03-28T01:03:43Z
dc.date.available	2024-03-28T01:03:43Z
dc.date.issued	2023-01-30
dc.identifier.citation	ACM International Conference Proceeding Series, 2023, pp. 183-189
dc.identifier.isbn	9798400700057
dc.identifier.uri	http://hdl.handle.net/10453/177311
dc.description.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.
dc.language	en
dc.publisher	ASSOC COMPUTING MACHINERY
dc.relation.ispartof	ACM International Conference Proceeding Series
dc.relation.ispartof	Australian Computer Science Week (ACSW)
dc.relation.isbasedon	10.1145/3579375.3579399
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	IR-ER- A Hybrid Pipeline for Classifying COVID-19 RNA Seq Data
dc.type	Conference Proceeding
utslib.location.activity	AUSTRALIA, Melbourne
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	recently_added	*
dc.date.updated	2024-03-28T01:03:42Z
pubs.finish-date	2023-02-03
pubs.publication-status	Published
pubs.start-date	2023-01-31

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/177311