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In-Cardiome: integrated knowledgebase for coronary artery disease enabling translational research

Coronary artery disease (CAD) is a leading cause of death worldwide. Prevention, diagnosis and clinical interventions are dependent on the conventional risk factors like hypertension, diabetes and obesity. However, these conventional risk factors do not completely identify high risk individuals. One...

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Autores principales: Sharma, Ankit, Deshpande, Vrushali, Ghatge, Madankumar, Vangala, Rajani Kanth
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5737197/
https://www.ncbi.nlm.nih.gov/pubmed/29220465
http://dx.doi.org/10.1093/database/bax077
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author Sharma, Ankit
Deshpande, Vrushali
Ghatge, Madankumar
Vangala, Rajani Kanth
author_facet Sharma, Ankit
Deshpande, Vrushali
Ghatge, Madankumar
Vangala, Rajani Kanth
author_sort Sharma, Ankit
collection PubMed
description Coronary artery disease (CAD) is a leading cause of death worldwide. Prevention, diagnosis and clinical interventions are dependent on the conventional risk factors like hypertension, diabetes and obesity. However, these conventional risk factors do not completely identify high risk individuals. One major hurdle in the improvement of diagnosis and treatment for CAD is the lack of integration of knowledge from different areas of research like molecular, clinical and drug development. In order to provide comprehensive information from hitherto dispersed data, we developed an integrative knowledgebase called “In-Cardiome or Integrated Cardiome” for all the stake holders in healthcare such as scientists, clinicians and pharmaceutical companies. It is created by integrating 16 different data sources, 995 curated genes classified into 12 different functional categories associated with disease, 1204 completed clinical trials, 12 therapy or drug classifications with 62 approved drugs and drug target networks. This knowledgebase gives the most needed opportunity to understand the disease process and therapeutic impact along with gene expression data from both animal models and patients. The data is classified into three different search categories functional groups, risk factors and therapy/drug based classes. One more unique aspect of In-Cardiome is integration of clinical data of 10,217 subject data from our ongoing Indian Atherosclerosis Research Study (IARS) (6357 unaffected and 3860 CAD affected). IARS data showing demographics and associations of individual and combinations of risk factors in Indian population along with molecular information will enable better translational and drug development research. DATABASE URL: www.tri-incardiome.org
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spelling pubmed-57371972018-01-08 In-Cardiome: integrated knowledgebase for coronary artery disease enabling translational research Sharma, Ankit Deshpande, Vrushali Ghatge, Madankumar Vangala, Rajani Kanth Database (Oxford) Original Article Coronary artery disease (CAD) is a leading cause of death worldwide. Prevention, diagnosis and clinical interventions are dependent on the conventional risk factors like hypertension, diabetes and obesity. However, these conventional risk factors do not completely identify high risk individuals. One major hurdle in the improvement of diagnosis and treatment for CAD is the lack of integration of knowledge from different areas of research like molecular, clinical and drug development. In order to provide comprehensive information from hitherto dispersed data, we developed an integrative knowledgebase called “In-Cardiome or Integrated Cardiome” for all the stake holders in healthcare such as scientists, clinicians and pharmaceutical companies. It is created by integrating 16 different data sources, 995 curated genes classified into 12 different functional categories associated with disease, 1204 completed clinical trials, 12 therapy or drug classifications with 62 approved drugs and drug target networks. This knowledgebase gives the most needed opportunity to understand the disease process and therapeutic impact along with gene expression data from both animal models and patients. The data is classified into three different search categories functional groups, risk factors and therapy/drug based classes. One more unique aspect of In-Cardiome is integration of clinical data of 10,217 subject data from our ongoing Indian Atherosclerosis Research Study (IARS) (6357 unaffected and 3860 CAD affected). IARS data showing demographics and associations of individual and combinations of risk factors in Indian population along with molecular information will enable better translational and drug development research. DATABASE URL: www.tri-incardiome.org Oxford University Press 2017-10-10 /pmc/articles/PMC5737197/ /pubmed/29220465 http://dx.doi.org/10.1093/database/bax077 Text en © The Author(s) 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Sharma, Ankit
Deshpande, Vrushali
Ghatge, Madankumar
Vangala, Rajani Kanth
In-Cardiome: integrated knowledgebase for coronary artery disease enabling translational research
title In-Cardiome: integrated knowledgebase for coronary artery disease enabling translational research
title_full In-Cardiome: integrated knowledgebase for coronary artery disease enabling translational research
title_fullStr In-Cardiome: integrated knowledgebase for coronary artery disease enabling translational research
title_full_unstemmed In-Cardiome: integrated knowledgebase for coronary artery disease enabling translational research
title_short In-Cardiome: integrated knowledgebase for coronary artery disease enabling translational research
title_sort in-cardiome: integrated knowledgebase for coronary artery disease enabling translational research
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5737197/
https://www.ncbi.nlm.nih.gov/pubmed/29220465
http://dx.doi.org/10.1093/database/bax077
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