Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/101917
Citations
Scopus Web of Science® Altmetric
?
?
Type: Journal article
Title: Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology
Author: Bakhtiarizadeh, M.
Moradi-Shahrbabak, M.
Ebrahimi, M.
Ebrahimie, E.
Citation: Journal of Theoretical Biology, 2014; 356:213-222
Publisher: Elsevier
Issue Date: 2014
ISSN: 0022-5193
1095-8541
Statement of
Responsibility: 
Mohammad Reza Bakhtiarizadeh, Mohammad Moradi-Shahrbabak, Mansour Ebrahimi, Esmaeil Ebrahimie
Abstract: Abstract not available
Keywords: Support vector machine; protein features; machine learning; lipid metabolism
Rights: © 2014 Elsevier Ltd. All rights reserved.
DOI: 10.1016/j.jtbi.2014.04.040
Appears in Collections:Aurora harvest 7
Medicine publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.