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Proteomic pattern analysis using a neural network application.

Authors
Chidambaram-S; Rao-KMK; Ahluwalia-RS
Source
FASEB J 2004 Mar; 18(5)(Suppl):A1220
NIOSHTIC No.
20031577
Abstract
To determine if exposure to diesel exhaust fumes causes a change in the serum proteomic patterns, serum samples were collected from 80 subjects (46 controls, 34 exposed). Serum proteomic profiles were performed on the Ciphergen Protein Chip® System using WCX2 chips. Proteomic patterns were analyzed by neural network techniques (classification and clustering algorithms) using "Predict" software obtained from Neuralware Inc. The Backpropagation algorithm was used as the classification algorithm and Self-Organizing Maps (SOM) was used as the clustering algorithm. Two methods were used for the identification of the most discriminating peaks. The first method used manual analysis of raw data using Euclidean distance as the criterion and the second method used a p-value statistic obtained from the Ciphergen software. The classification and clustering algorithms were applied to the two data sets. These procedures yielded a sensitivity of 82.5% and specificity of 81% using the peaks selected by the manual data analysis and a sensitivity of 90% and specificity of 92% using the peaks selected by the p-value analysis. These data indicate that a given serum profile pattern can be assigned to diesel exposure group at about 90% confidence limits using a neural network application.
Keywords
Serological-techniques; Mathematical-models; Analytical-processes; Analytical-chemistry; Analytical-methods; Analytical-models
Contact
NIOSH, PPRB, HELD, Morgantown, WV 26505
CODEN
FAJOEC
Publication Date
20040324
Document Type
Abstract; Conference/Symposia Proceedings
Fiscal Year
2004
NTIS Accession No.
NTIS Price
Issue of Publication
5
ISSN
0892-6638
NIOSH Division
HELD
Source Name
The FASEB Journal
State
WV
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