Two-tier combinatorial structure to integrate various gene co-expression networks of prostate cancer
Copyright © 2019 Elsevier B.V. All rights reserved..
Advances in DNA sequencing technologies enable researchers to integrate various biological datasets in order to reveal hidden relations at the molecular level. In this study, we present a two-tiered combinatorial structure (TTCS) to integrate gene co-expression networks (GCNs) that are inferred from microarray gene expression, RNA-Seq and miRNA-target gene data. In the initial phase of TTCS, we derive GCNs by using gene network inference (GNI) algorithms for each dataset. In the first and second integration phases, we use straightforward methods: intersection, union and simple majority voting to combine GCNs. We use overlap, topological and biological analyses in performance evaluation and investigate the integration effects of GCNs separately for all phases. Our results prove that the first integration phase has limited contribution on performance. However, combining the biological datasets in the second phase significantly enhances the overlap and topological performance analyses.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2019 |
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| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:721 |
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| Enthalten in: |
Gene - 721(2019) vom: 30. Dez., Seite 144102 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Cingiz, Mustafa Özgür [VerfasserIn] |
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| Links: |
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| Themen: |
Ensemble based decision making |
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| Anmerkungen: |
Date Completed 07.11.2019 Date Revised 07.11.2019 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.gene.2019.144102 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Advances in DNA sequencing technologies enable researchers to integrate various biological datasets in order to reveal hidden relations at the molecular level. In this study, we present a two-tiered combinatorial structure (TTCS) to integrate gene co-expression networks (GCNs) that are inferred from microarray gene expression, RNA-Seq and miRNA-target gene data. In the initial phase of TTCS, we derive GCNs by using gene network inference (GNI) algorithms for each dataset. In the first and second integration phases, we use straightforward methods: intersection, union and simple majority voting to combine GCNs. We use overlap, topological and biological analyses in performance evaluation and investigate the integration effects of GCNs separately for all phases. Our results prove that the first integration phase has limited contribution on performance. However, combining the biological datasets in the second phase significantly enhances the overlap and topological performance analyses | ||
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