NDLI: Comparison of discrimination methods for peptide classification in tandem mass spectrometry

Content Provider	IEEE Xplore Digital Library
Author	Bonner, A. Liu, H.
Copyright Year	2004
Description	Author affiliation: Dept. of Comput. Sci., Toronto Univ., Ont., Canada (Bonner, A.; Liu, H.)
Abstract	Proteomics - the direct analysis of the expressed protein components of a cell - is critical to our understanding of cellular biological processes. Key insights into the action and effects of a disease can be obtained by comparison of the expression of the expressed proteins in normal versus diseased tissue. Tandem mass spectrometry (MS/MS) of peptides is a central technology for Proteomics, enabling the identification of thousands of peptides from a complex mixture. With the increasing acquisition rate of tandem mass spectrometers, there is an increasing potential to solve important biological problems by applying data-mining and machine-learning techniques to MS/MS data. These problems include (i) estimating the levels of the thousands of proteins in a tissue sample, (ii) predicting the intensity of the peaks in a mass spectrum, and (iii) explaining why different peptides from the same protein have different peak intensities. In other works, we have focussed on the first two problems. In this paper, we focus on the last problem. In particular, we try to explain why some peptides produce peaks of great intensity, while others produce peaks of low intensity, and we treat this as a classification problem. That is, we experimentally evaluate and compare a variety of discrimination methods for classifying peptides into those that produce high-intensity peaks and those that produce low-intensity peaks. The methods considered include K-nearest neighbours (KNN), logistic regression, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), Naive Bayes, and hidden Markov models (HMMs). Experiments using these methods were conducted on three real-world datasets derived from tissue samples of Mouse. The methods were then evaluated using ROC curves and cross validation.
Sponsorship	IEEE Comput. Intelligence Soc
Starting Page	160
Ending Page	167
File Size	644884
Page Count	8
File Format	PDF
ISBN	0780387287
DOI	10.1109/CIBCB.2004.1393949
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-10-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Peptides Mass spectroscopy Proteins Proteomics Linear discriminant analysis Hidden Markov models Cells (biology) Biological processes Diseases Logistics
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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