dc.citation.conferencePlace |
KO |
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dc.citation.conferencePlace |
서울 |
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dc.citation.title |
TBC/BIOINFO 2018 (The 8th Annual Translational Bioinformatics Conference/2018 Annual Conference of Korean Society for Bioinformatics) |
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dc.contributor.author |
Yoon, Sora |
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dc.contributor.author |
Nguyen, Hai |
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dc.contributor.author |
Nam, Dougu |
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dc.date.accessioned |
2024-02-01T01:07:48Z |
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dc.date.available |
2024-02-01T01:07:48Z |
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dc.date.created |
2019-09-16 |
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dc.date.issued |
2018-10-31 |
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dc.description.abstract |
We present a novel approach to identify human microRNA (miRNA) targets for a variety of cell conditions by biclustering a large collection of mRNA fold-change data for sequencespecific targets. The bicluster targets exhibited on average 17.0% (median 19.4%) improved gain in certainty (sensitivity + specificity). The net gain was further increased up to 32.0% (median 33.2%) by incorporating functional networks of targets. We analyzed cancer-related biclusters and found that PI3K/Akt signaling pathway is strongly enriched in targets of a few miRNAs in breast cancer and diffuse large B-cell lymphoma. Among them, five independent prognostic miRNAs were identified, and repressions of bicluster targets and pathway activity by mir-29c were experimentally validated. In total, 29,898 biclusters for 459 human miRNAs were collected in BiMIR database, where biclusters are searchable for miRNAs, tissues, diseases, keywords, and target genes. |
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dc.identifier.bibliographicCitation |
TBC/BIOINFO 2018 (The 8th Annual Translational Bioinformatics Conference/2018 Annual Conference of Korean Society for Bioinformatics) |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/80592 |
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dc.publisher |
한국생물정보학회 |
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dc.title |
Biclustering Analysis of Transcriptomic Big Data Identifies Condition-specific microRNA targets |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2018-10-31 |
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