完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Wang, Hsiuying | en_US |
dc.contributor.author | Lu, Henry Horng-Shing | en_US |
dc.contributor.author | Chueh, Tung-Hung | en_US |
dc.date.accessioned | 2014-12-08T15:33:14Z | - |
dc.date.available | 2014-12-08T15:33:14Z | - |
dc.date.issued | 2011-06-01 | en_US |
dc.identifier.issn | 1932-6203 | en_US |
dc.identifier.uri | http://dx.doi.org/10.1371/journal.pone.0020074 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/23124 | - |
dc.description.abstract | Networks are widely used in biology to represent the relationships between genes and gene functions. In Boolean biological models, it is mainly assumed that there are two states to represent a gene: on-state and off-state. It is typically assumed that the relationship between two genes can be characterized by two kinds of pairwise relationships: similarity and prerequisite. Many approaches have been proposed in the literature to reconstruct biological relationships. In this article, we propose a two-step method to reconstruct the biological pathway when the binary array data have measurement error. For a pair of genes in a sample, the first step of this approach is to assign counting numbers for every relationship and select the relationship with counting number greater than a threshold. The second step is to calculate the asymptotic p-values for hypotheses of possible relationships and select relationships with a large p-value. This new method has the advantages of easy calculation for the counting numbers and simple closed forms for the p-value. The simulation study and real data example show that the two-step counting method can accurately reconstruct the biological pathway and outperform the existing methods. Compared with the other existing methods, this two-step method can provide a more accurate and efficient alternative approach for reconstructing the biological network. | en_US |
dc.language.iso | en_US | en_US |
dc.title | Constructing Biological Pathways by a Two-Step Counting Approach | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1371/journal.pone.0020074 | en_US |
dc.identifier.journal | PLOS ONE | en_US |
dc.citation.volume | 6 | en_US |
dc.citation.issue | 6 | en_US |
dc.citation.epage | en_US | |
dc.contributor.department | 統計學研究所 | zh_TW |
dc.contributor.department | Institute of Statistics | en_US |
dc.identifier.wosnumber | WOS:000291309900001 | - |
dc.citation.woscount | 3 | - |
顯示於類別: | 期刊論文 |