Automatic DNA microarray gridding based on Support Vector Machines

This paper presents a novel method for DNA microarray gridding based on Support Vector Machine (SVM) classifiers. It employs a set of soft-margin SVMs to estimate the lines of the DNA microarray grid by maximizing the margin between the lines and the spots. This process comprises an efficient and effective approach of separating the spots into distinct rows and columns. The classifiers are trained using the spot locations as training vectors. The results obtained from the application of the proposed method on reference microarray images illustrate its robustness in the presence of artifacts, noise and weakly expressed spots. The comparative evaluation presented reveals its advantageous performance over a state of the art gridding approach. The gridding quality achieved exceeds 95% in terms of the total number of perfectly gridded spots.

• Type: Conference
• Year: 2008
• Where: BIBE
• Authors: Dimitris G. Bariamis, Dimitris Maroulis, Dimitrios K. Iakovidis
• Keywords: BIBE 2008, Bioinformatics, DNA Microarray, Soft-margin Svms, Support Vector Machine