NDLI: Improving enzyme regulatory protein classification by means of SVM-RFE feature selection.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Fernandez-Lozano, Carlos Fernández-Blanco, Enrique Dave, Kirtan Pedreira, Nieves Gestal, Marcos Dorado, Julián Munteanu, Cristian R.
Description	Country affiliation: Spain Author Affiliation: Fernandez-Lozano C ( Computer Science Faculty, Dept. of Information and Communication Technologies, University of A Coruña, Campus de Elviña s/n, 15071 A Coruña, Spain. carlos.fernandez@udc.es.)
Abstract	Enzyme regulation proteins are very important due to their involvement in many biological processes that sustain life. The complexity of these proteins, the impossibility of identifying direct quantification molecular properties associated with the regulation of enzymatic activities, and their structural diversity creates the necessity for new theoretical methods that can predict the enzyme regulatory function of new proteins. The current work presents the first classification model that predicts protein enzyme regulators using the Markov mean properties. These protein descriptors encode the topological information of the amino acid into contact networks based on amino acid distances and physicochemical properties. MInD-Prot software calculated these molecular descriptors for 2415 protein chains (350 enzyme regulators) using five atom physicochemical properties (Mulliken electronegativity, Kang-Jhon polarizability, vdW area, atom contribution to P) and the protein 3D regions. The best classification models to predict enzyme regulators have been obtained with machine learning algorithms from Weka using 18 features. K* has been demonstrated to be the most accurate algorithm for this protein function classification. Wrapper Subset Evaluator and SVM-RFE approaches were used to perform a feature subset selection with the best results obtained from SVM-RFE. Classification performance employing all the available features can be reached using only the 8 most relevant features selected by SVM-RFE. Thus, the current work has demonstrated the possibility of predicting new molecular targets involved in enzyme regulation using fast theoretical algorithms.
File Format	HTM / HTML
ISSN	1742206X
Issue Number	5
Volume Number	10
e-ISSN	17422051
Journal	Molecular BioSystems
Language	English
Publisher	Royal Society of Chemistry
Publisher Date	2014-05-01
Publisher Place	Great Britain (UK)
Access Restriction	Subscribed
Subject Keyword	Discipline Molecular Biology Discipline Biochemistry Enzymes Metabolism Support Vector Machines Artificial Intelligence Databases, Protein Markov Chains Roc Curve Reference Standards Software Journal Article Research Support, Non-u.s. Gov't
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biotechnology

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in