dc.citation.conferencePlace |
CC |
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dc.citation.conferencePlace |
Sun Yat-sen medical school |
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dc.citation.title |
SYSU Symposium for the academic festival |
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dc.contributor.author |
Nam, Dougu |
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dc.date.accessioned |
2024-02-01T00:41:37Z |
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dc.date.available |
2024-02-01T00:41:37Z |
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dc.date.created |
2018-12-20 |
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dc.date.issued |
2018-12-03 |
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dc.description.abstract |
We present a novel approach to identify human microRNA (miRNA) regulatory modules (mRNA targets and relevant cell conditions) by biclustering a large collection of mRNA fold-change data for sequencespecific targets. Bicluster targets were assessed using validated messenger RNA (mRNA) targets and exhibited on an average 17.0% (median 19.4%) improved gain in certainty (sensitivity + specificity). The net gain was further increased up to 32.0% (median 33.4%) by incorporating functional networks of targets. We analyzed cancer-specific biclusters and found that the PI3K/Akt signaling pathway is strongly enriched with targets of a few miRNAs in breast cancer and diffuse large B-cell lymphoma. Indeed, five independent prognosticmiRNAs were identified, and repression of bicluster targets and pathway activity by miR-29 was experimentally validated. In total, 29 898 biclusters for 459 human miRNAs were collected in the BiMIR database where biclusters are searchable for miRNAs, tissues, diseases, keywords and target genes. |
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dc.identifier.bibliographicCitation |
SYSU Symposium for the academic festival |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/80321 |
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dc.language |
한국어 |
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dc.publisher |
Sun Yat-sen medical school |
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dc.title |
Biclustering analysis of transcriptome big data identifies cancer suppressing miRNAs |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2018-12-01 |
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