Open AccessBook
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.
read more
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.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
An apriori-based algorithm for mining semi-order-preserving submatrix
Yun Xue,Tiechen Li,Hao Lan Zhang,Xiaosheng Wu,Meihang Li,Xiaohui Hu +5 more
- 01 Jan 2016
TL;DR: A new model semi-order-preserving submatrix or SOPSM that can be generalised to cover most existing bicluster models is defined and a novel exact algorithm for mining all significant SOPSMs is proposed to reduce the computational costs.
Variable selection in kernel Fisher discriminant analysis by means of recursive feature elimination
N. Louw,S. J. Steel +1 more
TL;DR: The important problem of eliminating redundant input variables before implementing Kernel Fisher discriminant analysis (KFDA) is addressed and a backward elimination approach is recommended, and two criteria which can be used for recursive elimination of input variables are proposed and investigated.
Robust inference for parsimonious model-based clustering
TL;DR: An advantage of the resulting mtclust approach is that eigenvalue-ratio constraints are not needed for certain model formulations, leading to affine invariant robust parsimonious clustering.
Individualised risk prediction model for new-onset, progression and regression of chronic kidney disease in a retrospective cohort of patients with type 2 diabetes under primary care in Hong Kong
TL;DR: It is demonstrated that individual risks of new-onset and progression of CKD can be predicted from the routine physical and laboratory test results.
Parameterized Logistic Models for Bridge Inspection and Maintenance Scheduling
TL;DR: Proper inspection and maintenance schedules are integral to bridge functionality and safety; however, they also pose challenges in light of budget and resource limitations.