A survey of the Hough transform
Computer Vision, Graphics, and Image Processing
Error propagation for the Hough transform
Pattern Recognition Letters
Model selection for probabilistic clustering using cross-validatedlikelihood
Statistics and Computing
Hypothesis Testing: A Framework for Analyzing and Optimizing Hough Transform Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
A Bayesian approach to the Hough transform for line detection
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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The effects of a drug on the genomic scale can be assessed in a three-color cDNA microarray with the three color intensities represented through the so-called hexaMplot. In our recent study, we have shown that the Hough Transform (HT) applied to the hexaMplot can be used to detect groups of coexpressed genes in the normal-disease-drug samples. However, the standard HT is not well suited for the purpose because 1) the assayed genes need first to be hard-partitioned into equally and differentially expressed genes, with HT ignoring possible information in the former group; 2) the hexaMplot coordinates are negatively correlated and there is no direct way of expressing this in the standard HT and 3) it is not clear how to quantify the association of coexpressed genes with the line along which they cluster. We address these deficiencies by formulating a dedicated probabilistic model-based HT. The approach is demonstrated by assessing effects of the drug Rg1 on homocysteine-treated human umbilical vein endothetial cells. Compared with our previous study, we robustly detect stronger natural groupings of coexpressed genes. Moreover, the gene groups show coherent biological functions with high significance, as detected by the Gene Ontology analysis.