The Prediction of Peptide Detectability in MS Data Analysis Using Logistic Regression

Author

Liu, Hui ; Zhang, Jiyang ; Sun, Hanchang ; Xu, Changming ; Zhang, Wei ; Wang, Tengjiao ; Zhu, Yunping ; Xie, Hongwei

Author_Institution

Coll. of Mechatron. Eng. & Autom., Nat. Univ. of Defense Technol., Changsha, China

fYear

2011

fDate

10-12 May 2011

Firstpage

1

Lastpage

4

Abstract

The probability of the peptide that can be observed in the proteomics experiment based on mass spectrometry (MS) is not only determined by the abundance of proteins, but also heavily determined by the properties or structures of peptides. The set of peptides that are detected from a single protein could differ from one experiment to another substantially. We present an approach to predict the probability of the peptide that can be detected in MS-based proteomic experiment based on the logistic regression using the properties of peptides, and it has been tested and verified on the different datasets and showed satisfactory performance.

Keywords

logistics; mass spectroscopy; molecular biophysics; molecular configurations; proteins; proteomics; regression analysis; MS data analysis; logistic regression; mass spectrometry; peptide detectability; peptide structures; proteins; proteomics; Accuracy; Databases; Logistics; Peptides; Proteins; Proteomics; Spectroscopy;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Biomedical Engineering, (iCBBE) 2011 5th International Conference on

Conference_Location

Wuhan

ISSN

2151-7614

Print_ISBN

978-1-4244-5088-6

Type

conf

DOI

10.1109/icbbe.2011.5780167

Filename

5780167