Hybridization of blind source separation and rough sets for proteomic biomarker identification Conference Paper

abstract

• Biomarkers are molecular parameters associated with presence and severity of specific disease states. Search for biological markers of cancer in proteomic profiles is a relatively new but very active research area. This paper presents a novel approach to feature selection and thus biomarker identification. The proposed method is based on blind separation of sources and selection of features from a reduced set of components.

authors

• Suva, Larry

published proceedings

• ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING - ICAISC 2004

author list (cited authors)

• Boratyn, G. A., Smolinski, T. G., Zurada, J. M., Milanova, M., Bhattacharyya, S., & Suva, L. J.

citation count

• 1

complete list of authors

• Boratyn, GA||Smolinski, TG||Zurada, JM||Milanova, M||Bhattacharyya, S||Suva, LJ

publication date

• December 2004

publisher

• Springer Nature  Publisher

published in

• Lecture Notes in Artificial Intelligence  Journal

keywords

• 4 Detection, Screening And Diagnosis
• 4.1 Discovery And Preclinical Testing Of Markers And Technologies
• 46 Information And Computing Sciences
• Biotechnology
• Cancer

Digital Object Identifier (DOI)

• 10.1007/978-3-540-24844-6_72

International Standard Book Number (ISBN) 10

• 3-540-22123-9

International Standard Book Number (ISBN) 13

• 9783540221234

start page

• 486

end page

• 491

volume

• 3070

URL

• http://dx.doi.org/10.1007/978-3-540-24844-6_72