@article {10.3844/jcssp.2005.355.362,
article_type = {journal},
title = {A Recursive Application of a Support Vector Machine for Protein Spot Detection in 2-Dimensional Gel Electrophoresis},
author = {Boetticher, Gary D. and Al-Mubaid, Hisham and Frasier-Scott, Karen},
volume = {1},
number = {3},
year = {2005},
month = {Sep},
pages = {355-362},
doi = {10.3844/jcssp.2005.355.362},
url = {https://thescipub.com/abstract/jcssp.2005.355.362},
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.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}