Screening feature modules and pathways in glioma using EgoNet
Background: To investigate differential egonetwork modules and pathways in glioma using EgoNet algorithm. Methodology: Based on microarray data, EgoNet algorithm mainly comprised three stages: construction of differential co-expression network (DCN); EgoNet algorithm used to identify candidate ego-network modules based on the increased classification accuracy; statistical significance for candidate modules using random permutation testing. After that, pathway enrichment analysis for differential ego-network modules was implemented to illuminate the biological processes. Results: We obtained 109 ego genes. From every ego gene, we progressively grew the ego-networks by levels; we extracted 109 ego-networks and the mean node size in an ego-network was 6. By setting the classification accuracy threshold at 0.90 and the count of nodes in an ego-network module at 10, we extracted 8 candidate ego-network modules. After random permutation test with 1000 times, 5 modules including module 59, 72, 78, 86, and 90 were identified to be significant. Of note, the genes of module 90 and 86 were enriched in the pathway of resolution of sister chromatid cohesion and mitotic prometaphase, respectively. Conclusion: The identified modules and their corresponding ego genes might be beneficial in revealing the pathology underlying glioma and give insight for future research of glioma.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Screening feature modules and pathways in glioma using EgoNet ; volume:12 ; number:1 ; year:2017 ; pages:277-284 ; extent:8
Open life sciences ; 12, Heft 1 (2017), 277-284 (gesamt 8)
- Urheber
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He, Li
Song, Xian-Xu
Wang, Mei
Zhang, Ben-Zhuo
- DOI
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10.1515/biol-2017-0032
- URN
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urn:nbn:de:101:1-2409201643170.757767000679
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.01.2025, 00:00 MEZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- He, Li
- Song, Xian-Xu
- Wang, Mei
- Zhang, Ben-Zhuo
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