dc.citation.conferencePlace |
KO |
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dc.citation.conferencePlace |
여수 |
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dc.citation.title |
2018 포스트게놈 다부처 유전체사업 성과교류회 |
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dc.contributor.author |
Yoon, Sora |
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dc.contributor.author |
Nguyen, Hai C. T. |
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dc.contributor.author |
Jo, Woobeen |
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dc.contributor.author |
Kim, Jinhwan |
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dc.contributor.author |
Chi, Sang-Mun |
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dc.contributor.author |
Park, Jiyoung |
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dc.contributor.author |
Kim, Seon-Young |
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dc.contributor.author |
Nam, Dougu |
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dc.date.accessioned |
2024-02-01T01:05:54Z |
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dc.date.available |
2024-02-01T01:05:54Z |
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dc.date.created |
2019-09-16 |
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dc.date.issued |
2018-11-26 |
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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 |
2018 포스트게놈 다부처 유전체사업 성과교류회 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/80356 |
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dc.publisher |
한국연구재단 |
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dc.title |
Biclustering Analysis of Transcriptomic Big-Data Identifies Condition-specific miRNA targets |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2018-11-26 |
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