Unsupervised SVM-based gridding for DNA microarray images

This paper presents a novel method for unsupervised DNA microarray gridding based on Support Vector Machines (SVMs). Each spot is a small region on the microarray surface where chains of known DNA sequences are attached. The goal of microarray gridding is the separation of the spots into distinct cells. The positions of the spots on a DNA microarray image are first detected using image analysis operations and then a set of soft-margin linear SVM classifiers is used to estimate the optimal layout of the grid lines in the image. Each grid line is the separating line produced by one of the SVM classifiers, which maximizes the margin between two consecutive rows or columns of spots. The classifiers are trained using the spot locations as training vectors. The proposed method was evaluated on reference microarray images containing more than two million spots in total. The results illustrate its robustness in the presence of artifacts, noise and weakly expressed spots, as well as image rotat...

• Type: Journal
• Year: 2010
• Where: CMIG
• Authors: Dimitris G. Bariamis, Dimitris Maroulis, Dimitrios K. Iakovidis
• Keywords: CMIG 2010, DNA Microarray, Microarray Images, Support Vector Machines