A Recursive Application of a Support Vector Machine for Protein Spot Detection in 2-Dimensional Gel Electrophoresis

Abstract

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.