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※ [本文轉錄自 NCTU-STAT96G 看板]
作者: cyz ( ) 看板: NCTU-STAT96G
標題: 1221演講＠清大統研所
時間: Wed Dec 19 18:28:51 2007
清華大學、交通大學
統 計 學 研 究 所
專 題 演 講
題 目: A network biology approach for disease study
主講人: 楊永正 教授 (陽明大學生物醫學資訊研究所)
時 間: 96年12月21日（星期五）10：40 - 11：30
（上午10：20-10：40茶會於統計所821室舉行）
地 點: 清大綜合三館837室
Abstract
Interacting proteins may play similar roles in a biological process and lead
to the same phenotype or disease. Previous study showed that the disease gene
products of several Spinocerebellar ataxia (SCA) subtypes interact with each
other directly or indirectly. This observation supports the close relation
between protein interaction and the phenotype. We thus took advantage of this
relation to predict novel disease gene of other subtypes of SCA. In order to
assess the reliability, we used known SCA subtypes, e.g. SCA1 and SCA2, as
positive controls to test the performance of this approach. The known disease
genes of SCA1 SCA2, and SCA3 were found in the top two places in the
candidate list. The disease genes of other subtypes, such as SCA6, SCA7, were
ranked in the top 5th and 10th position in the candidate list, respectively.
Therefore, it is possible to apply this method to predict candidates for
those SCA subtypes, which do not have a disease gene yet. This new
network-based approach does not require any sequence feature, so this method
has the potential to predict candidate genes for other types of diseases.
In contrast to simple diseases, many risk genes are involved in the complex
diseases, such as schizophrenia. This disease was studied by linkage
analysis, association study, or even comparative genome hybridization. It’s
interesting to observe that the proteins encoded by these risk genes are
mutual neighbors in the human protein interaction network. Two protein
clusters were found on the basis of the distance among these proteins. These
clusters seem to be involved in neural transmission and neural development,
respectively. A possible pathway was proposed on the basis of protein
interactions, which might be useful in exploring the disease mechanism.
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