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- Publisher Website: 10.1109/CIBCB.2012.6217227
- Scopus: eid_2-s2.0-84864033158
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Conference Paper: An integrative bioinformatics approach for identifying subtypes and subtype-specific drivers in cancer
Title | An integrative bioinformatics approach for identifying subtypes and subtype-specific drivers in cancer |
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Authors | |
Issue Date | 2012 |
Publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001142 |
Citation | The 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB’12), San Diego, CA., 9-12 May 2012. In IEEE CIBCB Proceedings, 2012, p. 169-176 How to Cite? |
Abstract | Cancer is a complex disease and within a cancer, subtypes of patients with distinct behaviors often exist. The subtypes might have been caused by different hits, such as copy number aberrations (CNAs) and point mutations, on different pathways/cells-of-origin in a common tissue/organ. Identifying the subtypes with subtype-specific drivers, i.e., hits, is key to the understanding of cancer and development of novel treatments. Here, we report the development of an integrative method to identify the subtypes of cancer. Specifically, we consider CNAs and their impact on gene expressions. Based on these relations, we propose an iterative approach that alternates between kernel based gene expression clustering and gene signature selection. We applied the method to datasets of the pediatric cancer medulloblastoma (MB). The consensus number of clusters quickly converges to three; and for each of these three subtypes, the signature detection also converges to a consistent set of a few hundred highly functionally related genes. For each of the subtypes, we correlate its signature with the set of within-subtype recurrent CNA-affected genes for identifying drivers. The top-ranked driver candidates are found to be enriched with known pathways in certain subtypes of MB as well as containing novel genes that might reveal new understandings for other subtypes. |
Persistent Identifier | http://hdl.handle.net/10722/165157 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Chen, P | en_US |
dc.contributor.author | Hung, YS | en_US |
dc.contributor.author | Fan, Y | en_US |
dc.contributor.author | Wong, STC | en_US |
dc.date.accessioned | 2012-09-20T08:15:55Z | - |
dc.date.available | 2012-09-20T08:15:55Z | - |
dc.date.issued | 2012 | en_US |
dc.identifier.citation | The 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB’12), San Diego, CA., 9-12 May 2012. In IEEE CIBCB Proceedings, 2012, p. 169-176 | en_US |
dc.identifier.isbn | 978-1-4673-1191-5 | - |
dc.identifier.uri | http://hdl.handle.net/10722/165157 | - |
dc.description.abstract | Cancer is a complex disease and within a cancer, subtypes of patients with distinct behaviors often exist. The subtypes might have been caused by different hits, such as copy number aberrations (CNAs) and point mutations, on different pathways/cells-of-origin in a common tissue/organ. Identifying the subtypes with subtype-specific drivers, i.e., hits, is key to the understanding of cancer and development of novel treatments. Here, we report the development of an integrative method to identify the subtypes of cancer. Specifically, we consider CNAs and their impact on gene expressions. Based on these relations, we propose an iterative approach that alternates between kernel based gene expression clustering and gene signature selection. We applied the method to datasets of the pediatric cancer medulloblastoma (MB). The consensus number of clusters quickly converges to three; and for each of these three subtypes, the signature detection also converges to a consistent set of a few hundred highly functionally related genes. For each of the subtypes, we correlate its signature with the set of within-subtype recurrent CNA-affected genes for identifying drivers. The top-ranked driver candidates are found to be enriched with known pathways in certain subtypes of MB as well as containing novel genes that might reveal new understandings for other subtypes. | - |
dc.language | eng | en_US |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001142 | - |
dc.relation.ispartof | IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology Proceedings | en_US |
dc.title | An integrative bioinformatics approach for identifying subtypes and subtype-specific drivers in cancer | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Chen, P: h0795456@hku.hk | en_US |
dc.identifier.email | Hung, YS: yshung@hkucc.hku.hk | - |
dc.identifier.email | Fan, Y: yfan@tmhs.org | - |
dc.identifier.email | Wong, STC: stwong@tmhs.org | - |
dc.identifier.authority | Hung, YS=rp00220 | en_US |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/CIBCB.2012.6217227 | - |
dc.identifier.scopus | eid_2-s2.0-84864033158 | - |
dc.identifier.hkuros | 206404 | en_US |
dc.identifier.spage | 169 | - |
dc.identifier.epage | 176 | - |
dc.publisher.place | United States | - |
dc.description.other | The 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB’12), San Diego, CA., 9-12 May 2012. In IEEE CIBCB Proceedings, 2012, p. 169-176 | - |