完整後設資料紀錄
DC 欄位語言
dc.contributor.authorDoong, Shing-Hwang
dc.date.accessioned2009-08-23T04:51:01Z
dc.date.accessioned2020-05-29T06:38:39Z-
dc.date.available2009-08-23T04:51:01Z
dc.date.available2020-05-29T06:38:39Z-
dc.date.issued2008-07-22T06:06:40Z
dc.date.submitted2007-12-20
dc.identifier.urihttp://dspace.fcu.edu.tw/handle/2377/10742-
dc.description.abstractGene regulatory network modeling is a difficult inverse problem. Given limited amount of experimental data about gene expressions, a dynamic model is sought to fit the data to infer interesting biological processes. In this study, a well-known ecological system, the Lotka-Volterra system of differential equations, is used to model the dynamics of genes regulations. After replacing derivatives by estimated slopes, this system is decoupled into several independent systems of linear equations. Coefficients of the original Lotka-Volterra system are inferred from these linear systems by using multiple linear regressions. Two function approximation techniques, namely the cubic spline and the artificial neural network, are used to help estimate the stated slopes. It is found that the cubic spline interpolation and multiple linear regressions have provided useful solutions to the gene regulatory network problem.
dc.description.sponsorship亞洲大學資訊學院, 台中縣霧峰鄉
dc.format.extent10p.
dc.relation.ispartofseries2007 NCS會議
dc.subjectGene regulatory network
dc.subjectmultiple linear regressions
dc.subjectcubic spline interpolation
dc.subjectartificial neural network
dc.subject.otherGene Expression and Gene Networks
dc.titleGene Regulatory Network Modeling with Multiple Linear Regressions
分類:2007年 NCS 全國計算機會議

文件中的檔案:
檔案 描述 大小格式 
CE07NCS002007000028.pdf263.8 kBAdobe PDF檢視/開啟


在 DSpace 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。