A Comparison of PLS-based and other Dimension Reduction Methods for Tumour Classification using Microarray Data
Biotechnological development in the area of genomics has lead to an explosion in the amount of large datasets containing thousands of simultaneously measured gene or protein expressions from biological samples. Accordingly, there has been much activity in the development or re-deployment of many multivariate and data-mining techniques that may be used to analyze such data. A particular area of interest is tumour classification based on microarray data and many studies have compared various methods for this purpose. However, many of these studies compare methods based on a single benchmark dataset, or worse still compare methods from very different families where issues such as implicit data standardization (e.g. dissimilarity metric) might be responsible for differences in performance rather than the fundamental nature of the method. This study compares the classification accuracy of three different families of multivariate methods: 1.PLS-based methods; 2.Canonical ordination methods; and 3. Classification using scores obtained from
Microarray tumour classification Partial Least Squares gene ezpression
Cameron Hurst Janet Chaseling Michael Steele
Queensland University of Technology, Brisbane Australia Griffith University, Brisbane, Australia Bond University, Gold Coast, Australia
国际会议
The 6th International Conference on Partial Least Squares and Related Methods(第六届偏最小二乘及相关方法国际会议)
北京
英文
44-48
2009-09-04（万方平台首次上网日期，不代表论文的发表时间）