NDLI: RepCOOL: computational drug repositioning via integrating heterogeneous biological networks

Content Provider	Springer Nature : BioMed Central
Author	Fahimian, Ghazale Zahiri, Javad Arab, Seyed Shahriar Sajedi, Reza H.
Abstract	Background It often takes more than 10 years and costs more than 1 billion dollars to develop a new drug for a particular disease and bring it to the market. Drug repositioning can significantly reduce costs and time in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed. This methodology has widely been used in order to address various medical challenges, including cancer treatment. The most common cancers are lung and breast cancers. Thus, suggesting FDA-approved drugs via drug repositioning for breast cancer would help us to circumvent the approval process and subsequently save money as well as time. Methods In this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease. Results The proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. The final drug repositioning model has been built based on a random forest classifier after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II. Conclusion Results show the potency of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab, and Tamoxifen.
Related Links	https://translational-medicine.biomedcentral.com/counter/pdf/10.1186/s12967-020-02541-3.pdf
Ending Page	10
Page Count	10
Starting Page	1
File Format	HTM / HTML
ISSN	14795876
DOI	10.1186/s12967-020-02541-3
Journal	Journal of Translational Medicine
Issue Number	1
Volume Number	18
Language	English
Publisher	BioMed Central
Publisher Date	2020-10-02
Access Restriction	Open
Subject Keyword	Biomedicine Medicine Public Health Drug repositioning Drug-diseases interaction Biological network Network integration Machine learning Breast cancer Medicine/Public Health
Content Type	Text
Resource Type	Article
Subject	Biochemistry, Genetics and Molecular Biology Medicine
Journal Impact Factor	6.1/2023
5-Year Journal Impact Factor	6.3/2023

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

RepCOOL: Computational Drug Repositioning Via Integrating Heterogeneous Biological Networks

A Network Integration Approach for Drug-Target Interaction Prediction and Computational Drug Repositioning from Heterogeneous Information

DLDTI: a learning-based framework for drug-target interaction identification using neural networks and network representation

Drug repositioning for personalized medicine

Using machine learning to identify gene interaction networks associated with breast cancer

Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends

Drug repositioning targeting glutaminase reveals drug candidates for the treatment of Alzheimer’s disease patients

The integration of multidisciplinary approaches revealed PTGES3 as a novel drug target for breast cancer treatment

Radiomics-based machine learning analysis and characterization of breast lesions with multiparametric diffusion-weighted MR

RepCOOL: computational drug repositioning via integrating heterogeneous biological networks

Similar Documents

RepCOOL: Computational Drug Repositioning Via Integrating Heterogeneous Biological Networks

A Network Integration Approach for Drug-Target Interaction Prediction and Computational Drug Repositioning from Heterogeneous Information

DLDTI: a learning-based framework for drug-target interaction identification using neural networks and network representation

Drug repositioning for personalized medicine

Using machine learning to identify gene interaction networks associated with breast cancer

Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends

Drug repositioning targeting glutaminase reveals drug candidates for the treatment of Alzheimer’s disease patients

The integration of multidisciplinary approaches revealed PTGES3 as a novel drug target for breast cancer treatment

Radiomics-based machine learning analysis and characterization of breast lesions with multiparametric diffusion-weighted MR

RepCOOL: computational drug repositioning via integrating heterogeneous biological networks