.: Marcos Gestal :.     -----


Datos de la Publicación:

Carlos Fernandez-Lozano, Marcos Gestal, Nieves Pedreira, Lucian Postelnicu, Julián Dorado, Cristian Robert Munteanu
Título: Kernel-based Feature Selection Techniques for Transport Proteins Based on Star Graph Topological Indices
Revista: Current Topics in Medicinal Chemistry
ISSN: 1568-0266
Volumen: 13(14)
Páginas: 1681-1691
Editorial: Bentham Science Publishers
Fecha Publicación: Julio 2013
Factor de Impacto: 3.453
SCIMago Journal Rank: 1.170
PubMed ID: 23889046
doi: 10.2174/15680266113139990119
Categorías WoS: Chemistry, Medicinal - Cuartil: Q1 - Tercil: T1 - Posición 11 de 58
Citas ISI: 1
Citas Scopus: 1
Citas Google Scholar: 1


The transport of the molecules inside cells is a very important topic, especially in Drug Metabolism. The experimental testing of the new proteins for the transporter molecular function is expensive and inefficient due to the large amount of new peptides. Therefore, there is a need for cheap and fast theoretical models to predict the transporter proteins. In the current work, the primary structure of a protein is represented as a molecular Star graph, characterized by a series of topological indices. The dataset was made up of 2,503 protein chains, out of which 413 have transporter molecular function and 2,090 have no transporter function. These indices were used as input to several classification techniques to find the best Quantitative Structure Activity Relationship (QSAR) model that can evaluate the transporter function of a new protein chain. Among several feature selection techniques, the Support Vector Machine Recursive Feature Elimination allows us to obtain a classification model based on 20 attributes with a true positive rate of 83% and a false positive rate of 16.7%.