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Title 

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

Authors 

J JungTaewoo RyuY HwangE LeeD Lee

Publisher 

Mary Ann Liebert

Issue Date 

2010

Citation 

Journal of Computational Biology, vol. 17, no. 1, pp. 97-105

Keywords 

ECMExtracellular matrix proteinsProtein localizationRandom ForestSupport vector machine

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.

ISSN 

1066-5277

Link 

http://dx.doi.org/10.1089/cmb.2008.0236

Appears in Collections

1. Journal Articles > Journal Articles

Registered Date

2017-04-19


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