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Analyzing Microarray Gene Expression Data
Geoffrey J. McLachlan,Kim Anh Do,Christophe Ambroise +2 more
- 04 Aug 2004
875
TL;DR: In this article, the authors proposed a supervised classification of Tissue Samples and linked the supervised classification with survival analysis, and showed that the classification of tissue samples is more accurate than that of microarray data.
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Abstract: Preface. 1. Microarrays in Gene Expression Studies. 2. Cleaning and Normalization. 3. Some Cluster Analysis Methods. 4. Clustering of Tissue Samples. 5. Screening and Clustering of Genes. 6. Discriminant Analysis. 7. Supervised Classification of Tissue Samples. 8. Linking Microarray Data with Survival Analysis. References. Author Index. Subject Index.
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Prevalence and determinants of declining versus stable hemoglobin levels in whole blood donors.
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Applications of Diffusion Maps in Gene Expression Data-Based Cancer Diagnosis Analysis
Rui Xu,Steven B. Damelin,Donald C. Wunsch +2 more
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TL;DR: Wang et al. as discussed by the authors used diffusion maps, which interpret the eigenfunctions of Markov matrices as a system of coordinates on the original data set in order to obtain efficient representation of data geometric descriptions, for dimensionality reduction.
Efficient and Robust Syslog Parsing for Network Devices in Datacenter Networks
Shenglin Zhang,Lei Song,Ming Zhang,Ying Liu,Weibin Meng,Jiahao Bu,Sen Yang,Yongqian Sun,Dan Pei,Jun Xu,Yuzhi Zhang +10 more
TL;DR: Craftsman’s superior generality is demonstrated by comparing it with three widely-applied log parsing methods over five large log datasets collected from servers, distributed systems and applications.
A Methodology for Clustering Lakes in Alberta on the basis of Water Quality Parameters
TL;DR: In this article, a methodology for clustering 18 lakes in Alberta, Canada using the data of 19 water quality parameters for a period of 11 years (1988-2002) is presented.
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