A Recursive Application of a Support Vector Machine for Protein Spot Detection in 2-Dimensional Gel Electrophoresis
Gary D. Boetticher, Hisham Al-Mubaid and Karen Frasier-Scott
DOI : 10.3844/jcssp.2005.355.362
Journal of Computer Science
Volume 1, Issue 3
Two-dimensional polyacrylamide gel electrophoresis (2-D PAGE) analysis remains the core of proteomic technology because it is currently the most powerful method to analyze large collections of proteins. Advances in electrophoresis equipment are making this technique more accessible but effective computer assisted protein spot detection remains a very labor-intensive endeavor. Protein spot analysis is still time consuming, requires human intervention and is in need of further development. This study explores a technique of recursively applying a Support Vector Machine (SVM) in identifying protein. An SVM is a powerful learner capable of optimizing differences between classes. In this context the different classes correspond to the presence/absence of a protein. Different experiments are conducted to assess these differences in class formation in the context of a normal image and a highly saturated image.
© 2005 Gary D. Boetticher, Hisham Al-Mubaid and Karen Frasier-Scott. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.