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Integrative Meta-analysis of Transcriptome Data for Unmasking Biological Mechanism of Idiopathic pulmonary fibrosis
- Issue Date
- 5-Dec-2020
- Publisher
- IEEE
- Keywords
- Bioinformatics; Idiopathic pulmonary fibrosis; Genetic Markers; RNA-Seq; Meta-analysis
- Citation
- 2020 JOINT 11TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS AND 21ST INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS (SCIS-ISIS), pp 276 - 278
- Pages
- 3
- Indexed
- SCOPUS
- Journal Title
- 2020 JOINT 11TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS AND 21ST INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS (SCIS-ISIS)
- Start Page
- 276
- End Page
- 278
- ISSN
- 2377-6870
- Abstract
- Idiopathic pulmonary fibrosis is one of the chronic and fatal interstitial lung diseases. IPF generally shows poor prognosis, and their exact pathogenesis and casualties are not clearly revealed yet. RNA sequencing and microarray experiments enable the determination of genes whose expression levels are significantly different in IPF disease group compared with the healthy control group. Total 749 genes were identified as differentially expressed genes in both two data sets via (P-value < 0.05) via oligo, limma, and DESeq R packages. Among total DEGs, 453 genes were significantly up-regulated genes and 250 genes were down-regulated genes. In order to confirm the systemic functions of the obtained DEGs, we performed gene set enrichment analysis and functional annotation by database for Annotation, Visualization, and Integrated Discovery.
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Related Researcher
- Joo, Jong Wha Joanne
- College of Advanced Convergence Engineering (Department of Computer Science and Artificial Intelligence)