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
A vertex similarity index for better personalized recommendation
TL;DR: This work proposes a novel vertex similarity index named CosRA, which combines advantages of both the cosine index and the resource-allocation (RA) index and shows that the CosRA-based method has better performance in accuracy, diversity and novelty than some benchmark methods.
Quasi-supervised learning for biomedical data analysis
TL;DR: The fitness of the method in biomedical data analysis was further demonstrated on real multi-color flow cytometry and multi-channel electroencephalography data.
Prediction of activity cliffs using support vector machines.
TL;DR: A key aspect of the approach has been the design of new kernels to enable SVM classification on the basis of molecule pairs, rather than individual compounds, which has been accurately predicted using specifically designed structural representations and kernel functions.
A Novel Residual Electrical Endurance Prediction Method for Low-Voltage Electromagnetic Alternating Current Contactors
TL;DR: In this paper, a residual electrical endurance (REE) prediction method for low-voltage electromagnetic alternating current (ac) contactors based on conditional density estimation (CDE) is proposed.
Determination of the Cu(III)-OH Bond Distance by Resonance Raman Spectroscopy Using a Normalized Version of Badger's Rule.
TL;DR: A modified version of the Badger's rule was developed through use of stretching frequencies normalized by dividing by the appropriate reduced masses and found to yield excellent fits of normalized frequencies to bond distances for >250 data points from theory and experiment.