Prediction of extracellular matrix proteins based on distinctive sequence and domain characteristics

Cited 15 time in scopus
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Title
Prediction of extracellular matrix proteins based on distinctive sequence and domain characteristics
Author(s)
J Jung; Taewoo Ryu; Y Hwang; E Lee; D Lee
Bibliographic Citation
Journal of Computational Biology, vol. 17, no. 1, pp. 97-105
Publication Year
2010
Abstract
Extracellular matrix (ECM) proteins are secreted to the exterior of the cell, and function as mediators between resident cells and the external environment. These proteins not only support cellular structure but also participate in diverse processes, including growth, hormonal response, homeostasis, and disease progression. Despite their importance, current knowledge of the number and functions of ECM proteins is limited. Here, we propose a computational method to predict ECM proteins. Specific features, such as ECM domain score and repetitive residues, were utilized for prediction. Based on previously employed and newly generated features, discriminatory characteristics for ECM protein categorization were determined, which significantly improved the performance of Random Forest and support vector machine (SVM) classification. We additionally predicted novel ECM proteins from non-annotated human proteins, validated with gene ontology and earlier literature. Our novel prediction method is available at biosoft.kaist.ac.kr/ecm.
Keyword
ECMExtracellular matrix proteinsProtein localizationRandom ForestSupport vector machine
ISSN
1066-5277
Publisher
Mary Ann Liebert, Inc
DOI
http://dx.doi.org/10.1089/cmb.2008.0236
Type
Article
Appears in Collections:
1. Journal Articles > Journal Articles
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