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Proceedings Paper

Support vector machine based classification of fast Fourier transform spectroscopy of proteins
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Paper Abstract

Fast Fourier transform spectroscopy has proved to be a powerful method for study of the secondary structure of proteins since peak positions and their relative amplitude are affected by the number of hydrogen bridges that sustain this secondary structure. However, to our best knowledge, the method has not been used yet for identification of proteins within a complex matrix like a blood sample. The principal reason is the apparent similarity of protein infrared spectra with actual differences usually masked by the solvent contribution and other interactions. In this paper, we propose a novel machine learning based method that uses protein spectra for classification and identification of such proteins within a given sample. The proposed method uses principal component analysis (PCA) to identify most important linear combinations of original spectral components and then employs support vector machine (SVM) classification model applied on such identified combinations to categorize proteins into one of given groups. Our experiments have been performed on the set of four different proteins, namely: Bovine Serum Albumin, Leptin, Insulin-like Growth Factor 2 and Osteopontin. Our proposed method of applying principal component analysis along with support vector machines exhibits excellent classification accuracy when identifying proteins using their infrared spectra.

Paper Details

Date Published: 24 February 2009
PDF: 8 pages
Proc. SPIE 7169, Advanced Biomedical and Clinical Diagnostic Systems VII, 71690C (24 February 2009); doi: 10.1117/12.809964
Show Author Affiliations
Aleksandar Lazarevic, Delaware State Univ. (United States)
Dragoljub Pokrajac, Delaware State Univ. (United States)
Aristides Marcano, Delaware State Univ. (United States)
Noureddine Melikechi, Delaware State Univ. (United States)


Published in SPIE Proceedings Vol. 7169:
Advanced Biomedical and Clinical Diagnostic Systems VII
Anita Mahadevan-Jansen; Tuan Vo-Dinh; Warren S. Grundfest M.D., Editor(s)

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