NDLI: DrugNet: network-based drug-disease prioritization by integrating heterogeneous data.

Please wait, while we are loading the content...

Self-focusing therapeutic gene delivery with intelligent gene vector swarms: intra-swarm signalling through receptor transgene expression in targeted cells.

Improving the Mann-Whitney statistical test for feature selection: an approach in breast cancer diagnosis on mammography.

Predicting the risk of exacerbation in patients with chronic obstructive pulmonary disease using home telehealth measurement data.

DrugNet: network-based drug-disease prioritization by integrating heterogeneous data.

Year: 2014 Volume: 62

Year: 2014 Volume: 61

Year: 2014 Volume: 60

Year: 2013 Volume: 59

Year: 2013 Volume: 58

Year: 2013 Volume: 57

Year: 2012 Volume: 56

Year: 2012 Volume: 55

Year: 2012 Volume: 54

Year: 2011 Volume: 53

Year: 2011 Volume: 52

Year: 2011 Volume: 51

Year: 2010 Volume: 50

Year: 2010 Volume: 49

Year: 2010 Volume: 48

Year: 2009 Volume: 47

Year: 2009 Volume: 46

Year: 2009 Volume: 45

Year: 2008 Volume: 44

Year: 2008 Volume: 43

Year: 2008 Volume: 42

Year: 2007 Volume: 39

DrugNet: network-based drug-disease prioritization by integrating heterogeneous data.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Martínez, Víctor Navarro, Carmen Cano, Carlos Fajardo, Waldo Blanco, Armando
Description	Author Affiliation: Martínez V ( Department of Computer Science and Artificial Intelligence, University of Granada, C/ Daniel Saucedo Aranda S.N., 18071 Granada, Spain. Electronic address: fvictor@decsai.ugr.es.); Navarro C ( Department of Computer Science and Artificial Intelligence, University of Granada, C/ Daniel Saucedo Aranda S.N., 18071 Granada, Spain. Electronic address: cnluzon@decsai.ugr.es.); Cano C ( Department of Computer Science and Artificial Intelligence, University of Granada, C/ Daniel Saucedo Aranda S.N., 18071 Granada, Spain. Electronic address: ccano@decsai.ugr.es.); Fajardo W ( Department of Computer Science and Artificial Intelligence, University of Granada, C/ Daniel Saucedo Aranda S.N., 18071 Granada, Spain. Electronic address: aragorn@ugr.es.); Blanco A ( Department of Computer Science and Artificial Intelligence, University of Granada, C/ Daniel Saucedo Aranda S.N., 18071 Granada, Spain. Electronic address: armando@decsai.ugr.es.)
Abstract	OBJECTIVE: Computational drug repositioning can lead to a considerable reduction in cost and time in any drug development process. Recent approaches have addressed the network-based nature of biological information for performing complex prioritization tasks. In this work, we propose a new methodology based on heterogeneous network prioritization that can aid researchers in the drug repositioning process. METHODS: We have developed DrugNet, a new methodology for drug-disease and disease-drug prioritization. Our approach is based on a network-based prioritization method called ProphNet which has recently been developed by the authors. ProphNet is able to integrate data from complex networks involving a wide range of types of elements and interactions. In this work, we built a network of interconnected drugs, proteins and diseases and applied DrugNet to different types of tests for drug repositioning. RESULTS: We tested the performance of our approach on different validation tests, including cross validation and tests based on real clinical trials. DrugNet achieved a mean AUC value of 0.9552±0.0015 in 5-fold cross validation tests, and a mean AUC value of 0.8364 for tests based on recent clinical trials (phases 0-4) not present in our data. These results suggest that DrugNet could be very useful for discovering new drug uses. We also studied specific cases of particular interest, proving the benefits of heterogeneous data integration in this problem. CONCLUSIONS: Our methodology suggests that new drugs can be repositioned by generating ranked lists of drugs based on a given disease query or vice versa. Our study shows that the simultaneous integration of information about diseases, drugs and targets can lead to a significant improvement in drug repositioning tasks. DrugNet is available as a web tool from http://genome2.ugr.es/drugnet/ (accessed 23.09.14). Matlab source code is also available on the website.
File Format	HTM / HTML
ISSN	09333657
Issue Number	1
Volume Number	63
e-ISSN	18732860
Journal	Artificial Intelligence in Medicine
Language	English
Publisher	Elsevier
Publisher Date	2015-01-01
Publisher Place	Netherlands
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Discipline Medical Informatics Computational Biology Data Mining Databases, Factual Drug Repositioning Methods Systems Integration Area Under Curve Computer Simulation Humans Models, Theoretical Roc Curve Reproducibility Of Results Journal Article Research Support, Non-u.s. Gov't Validation Studies
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Medicine

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

A kernel-based framework to tensorial data analysis.

Random forests ensemble classifier trained with data resampling strategy to improve cardiac arrhythmia diagnosis.

Multiple kernel learning in protein-protein interaction extraction from biomedical literature.

Missing data in medical databases: impute, delete or classify?

Rating organ failure via adverse events using data mining in the intensive care unit.

eXiT*CBR: A framework for case-based medical diagnosis development and experimentation.

Predicting cardiac autonomic neuropathy category for diabetic data with missing values.

Prediction of flavin mono-nucleotide binding sites using modified PSSM profile and ensemble support vector machine.

Validating a two-high-threshold measurement model for confidence rating data in recognition.

DrugNet: network-based drug-disease prioritization by integrating heterogeneous data.

Similar Documents

A kernel-based framework to tensorial data analysis.

Random forests ensemble classifier trained with data resampling strategy to improve cardiac arrhythmia diagnosis.

Multiple kernel learning in protein-protein interaction extraction from biomedical literature.

Missing data in medical databases: impute, delete or classify?

Rating organ failure via adverse events using data mining in the intensive care unit.

eXiT*CBR: A framework for case-based medical diagnosis development and experimentation.

Predicting cardiac autonomic neuropathy category for diabetic data with missing values.

Prediction of flavin mono-nucleotide binding sites using modified PSSM profile and ensemble support vector machine.

Validating a two-high-threshold measurement model for confidence rating data in recognition.

DrugNet: network-based drug-disease prioritization by integrating heterogeneous data.