EcmPred: Prediction of extracellular matrix proteins based on random forest with maximum relevance minimum redundancy feature selection

Krishna Kumar Umar Kandaswamy; Pugalenthi Ganesan; Kai Uwe Kalies; Enno Hartmann; Thomas M. Martinetz

doi:10.1016/j.jtbi.2012.10.015

EcmPred: Prediction of extracellular matrix proteins based on random forest with maximum relevance minimum redundancy feature selection

Krishna Kumar Umar Kandaswamy, Pugalenthi Ganesan, Kai Uwe Kalies, Enno Hartmann, Thomas M. Martinetz

Bioscience Core Laboratory

Research output: Contribution to journal › Article › peer-review

23 Scopus citations

Abstract

The extracellular matrix (ECM) is a major component of tissues of multicellular organisms. It consists of secreted macromolecules, mainly polysaccharides and glycoproteins. Malfunctions of ECM proteins lead to severe disorders such as marfan syndrome, osteogenesis imperfecta, numerous chondrodysplasias, and skin diseases. In this work, we report a random forest approach, EcmPred, for the prediction of ECM proteins from protein sequences. EcmPred was trained on a dataset containing 300 ECM and 300 non-ECM and tested on a dataset containing 145 ECM and 4187 non-ECM proteins. EcmPred achieved 83% accuracy on the training and 77% on the test dataset. EcmPred predicted 15 out of 20 experimentally verified ECM proteins. By scanning the entire human proteome, we predicted novel ECM proteins validated with gene ontology and InterPro. The dataset and standalone version of the EcmPred software is available at http://www.inb.uni-luebeck.de/tools-demos/Extracellular_matrix_proteins/EcmPred. © 2012 Elsevier Ltd.

Original language	English (US)
Pages (from-to)	377-383
Number of pages	7
Journal	Journal of Theoretical Biology
Volume	317
DOIs	https://doi.org/10.1016/j.jtbi.2012.10.015
State	Published - Jan 2013

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported by the Graduate School for Computing in Medicine and Life Sciences funded by Germany's Excellence Initiative [DFG GSC 235/1]. KKK acknowledges Dr. Bianca Habermann, Max Planck Institute for Biology of Ageing, Germany for her support.

ASJC Scopus subject areas

General Agricultural and Biological Sciences
General Biochemistry, Genetics and Molecular Biology
Modeling and Simulation
Applied Mathematics
Statistics and Probability
General Immunology and Microbiology
General Medicine

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.jtbi.2012.10.015

Cite this

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title = "EcmPred: Prediction of extracellular matrix proteins based on random forest with maximum relevance minimum redundancy feature selection",

abstract = "The extracellular matrix (ECM) is a major component of tissues of multicellular organisms. It consists of secreted macromolecules, mainly polysaccharides and glycoproteins. Malfunctions of ECM proteins lead to severe disorders such as marfan syndrome, osteogenesis imperfecta, numerous chondrodysplasias, and skin diseases. In this work, we report a random forest approach, EcmPred, for the prediction of ECM proteins from protein sequences. EcmPred was trained on a dataset containing 300 ECM and 300 non-ECM and tested on a dataset containing 145 ECM and 4187 non-ECM proteins. EcmPred achieved 83% accuracy on the training and 77% on the test dataset. EcmPred predicted 15 out of 20 experimentally verified ECM proteins. By scanning the entire human proteome, we predicted novel ECM proteins validated with gene ontology and InterPro. The dataset and standalone version of the EcmPred software is available at http://www.inb.uni-luebeck.de/tools-demos/Extracellular_matrix_proteins/EcmPred. {\textcopyright} 2012 Elsevier Ltd.",

author = "Kandaswamy, {Krishna Kumar Umar} and Pugalenthi Ganesan and Kalies, {Kai Uwe} and Enno Hartmann and Martinetz, {Thomas M.}",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: This work was supported by the Graduate School for Computing in Medicine and Life Sciences funded by Germany's Excellence Initiative [DFG GSC 235/1]. KKK acknowledges Dr. Bianca Habermann, Max Planck Institute for Biology of Ageing, Germany for her support.",

year = "2013",

month = jan,

doi = "10.1016/j.jtbi.2012.10.015",

language = "English (US)",

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journal = "Journal of Theoretical Biology",

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AU - Kandaswamy, Krishna Kumar Umar

AU - Ganesan, Pugalenthi

AU - Kalies, Kai Uwe

AU - Hartmann, Enno

AU - Martinetz, Thomas M.

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: This work was supported by the Graduate School for Computing in Medicine and Life Sciences funded by Germany's Excellence Initiative [DFG GSC 235/1]. KKK acknowledges Dr. Bianca Habermann, Max Planck Institute for Biology of Ageing, Germany for her support.

PY - 2013/1

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N2 - The extracellular matrix (ECM) is a major component of tissues of multicellular organisms. It consists of secreted macromolecules, mainly polysaccharides and glycoproteins. Malfunctions of ECM proteins lead to severe disorders such as marfan syndrome, osteogenesis imperfecta, numerous chondrodysplasias, and skin diseases. In this work, we report a random forest approach, EcmPred, for the prediction of ECM proteins from protein sequences. EcmPred was trained on a dataset containing 300 ECM and 300 non-ECM and tested on a dataset containing 145 ECM and 4187 non-ECM proteins. EcmPred achieved 83% accuracy on the training and 77% on the test dataset. EcmPred predicted 15 out of 20 experimentally verified ECM proteins. By scanning the entire human proteome, we predicted novel ECM proteins validated with gene ontology and InterPro. The dataset and standalone version of the EcmPred software is available at http://www.inb.uni-luebeck.de/tools-demos/Extracellular_matrix_proteins/EcmPred. © 2012 Elsevier Ltd.

AB - The extracellular matrix (ECM) is a major component of tissues of multicellular organisms. It consists of secreted macromolecules, mainly polysaccharides and glycoproteins. Malfunctions of ECM proteins lead to severe disorders such as marfan syndrome, osteogenesis imperfecta, numerous chondrodysplasias, and skin diseases. In this work, we report a random forest approach, EcmPred, for the prediction of ECM proteins from protein sequences. EcmPred was trained on a dataset containing 300 ECM and 300 non-ECM and tested on a dataset containing 145 ECM and 4187 non-ECM proteins. EcmPred achieved 83% accuracy on the training and 77% on the test dataset. EcmPred predicted 15 out of 20 experimentally verified ECM proteins. By scanning the entire human proteome, we predicted novel ECM proteins validated with gene ontology and InterPro. The dataset and standalone version of the EcmPred software is available at http://www.inb.uni-luebeck.de/tools-demos/Extracellular_matrix_proteins/EcmPred. © 2012 Elsevier Ltd.

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EcmPred: Prediction of extracellular matrix proteins based on random forest with maximum relevance minimum redundancy feature selection

Abstract

Bibliographical note

ASJC Scopus subject areas

UN SDGs

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