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Integrated ACMG-approved genes and ICD codes for the translational research and precision medicine

A timely understanding of the biological secrets of complex diseases will ultimately benefit millions of individuals by reducing the high risks for mortality and improving the quality of life with personalized diagnoses and treatments. Due to the advancements in sequencing technologies and reduced c...

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Autores principales: Wable, Raghunandan, Nair, Achuth Suresh, Pappu, Anirudh, Pierre-Louis, Widnie, Abdelhalim, Habiba, Patel, Khushbu, Mendhe, Dinesh, Bolla, Shreyas, Mittal, Sahil, Ahmed, Zeeshan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10191139/
https://www.ncbi.nlm.nih.gov/pubmed/37195695
http://dx.doi.org/10.1093/database/baad033
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author Wable, Raghunandan
Nair, Achuth Suresh
Pappu, Anirudh
Pierre-Louis, Widnie
Abdelhalim, Habiba
Patel, Khushbu
Mendhe, Dinesh
Bolla, Shreyas
Mittal, Sahil
Ahmed, Zeeshan
author_facet Wable, Raghunandan
Nair, Achuth Suresh
Pappu, Anirudh
Pierre-Louis, Widnie
Abdelhalim, Habiba
Patel, Khushbu
Mendhe, Dinesh
Bolla, Shreyas
Mittal, Sahil
Ahmed, Zeeshan
author_sort Wable, Raghunandan
collection PubMed
description A timely understanding of the biological secrets of complex diseases will ultimately benefit millions of individuals by reducing the high risks for mortality and improving the quality of life with personalized diagnoses and treatments. Due to the advancements in sequencing technologies and reduced cost, genomics data are developing at an unmatched pace and levels to foster translational research and precision medicine. Over 10 million genomics datasets have been produced and publicly shared in 2022. Diverse and high-volume genomics and clinical data have the potential to broaden the scope of biological discoveries and insights by extracting, analyzing and interpreting the hidden information. However, the current and still unresolved challenges include the integration of genomic profiles of the patients with their medical records. The definition of disease in genomics medicine is simplified, whereas in the clinical world, diseases are classified, identified and adopted with their International Classification of Diseases (ICD) codes, which are maintained by the World Health Organization. Several biological databases have been produced, which include information about human genes and related diseases. However, still, there is no database that exists, which can precisely link clinical codes with relevant genes and variants to support genomic and clinical data integration for clinical and translational medicine. In this project, we focused on the development of an annotated gene–disease–code database, which is accessible through an online, cross-platform and user-friendly application, i.e. PROMIS-APP-SUITE-Gene-Disease-Code. However, our scope is limited to the integration of ICD-9 and ICD-10 codes with the list of genes approved by the American College of Medical Genetics and Genomics. The results include over 17 000 diseases and 4000 ICD codes, and over 11 000 gene–disease–code combinations. Database URL https://promis.rutgers.edu/pas/
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spelling pubmed-101911392023-05-18 Integrated ACMG-approved genes and ICD codes for the translational research and precision medicine Wable, Raghunandan Nair, Achuth Suresh Pappu, Anirudh Pierre-Louis, Widnie Abdelhalim, Habiba Patel, Khushbu Mendhe, Dinesh Bolla, Shreyas Mittal, Sahil Ahmed, Zeeshan Database (Oxford) Database Tool A timely understanding of the biological secrets of complex diseases will ultimately benefit millions of individuals by reducing the high risks for mortality and improving the quality of life with personalized diagnoses and treatments. Due to the advancements in sequencing technologies and reduced cost, genomics data are developing at an unmatched pace and levels to foster translational research and precision medicine. Over 10 million genomics datasets have been produced and publicly shared in 2022. Diverse and high-volume genomics and clinical data have the potential to broaden the scope of biological discoveries and insights by extracting, analyzing and interpreting the hidden information. However, the current and still unresolved challenges include the integration of genomic profiles of the patients with their medical records. The definition of disease in genomics medicine is simplified, whereas in the clinical world, diseases are classified, identified and adopted with their International Classification of Diseases (ICD) codes, which are maintained by the World Health Organization. Several biological databases have been produced, which include information about human genes and related diseases. However, still, there is no database that exists, which can precisely link clinical codes with relevant genes and variants to support genomic and clinical data integration for clinical and translational medicine. In this project, we focused on the development of an annotated gene–disease–code database, which is accessible through an online, cross-platform and user-friendly application, i.e. PROMIS-APP-SUITE-Gene-Disease-Code. However, our scope is limited to the integration of ICD-9 and ICD-10 codes with the list of genes approved by the American College of Medical Genetics and Genomics. The results include over 17 000 diseases and 4000 ICD codes, and over 11 000 gene–disease–code combinations. Database URL https://promis.rutgers.edu/pas/ Oxford University Press 2023-05-17 /pmc/articles/PMC10191139/ /pubmed/37195695 http://dx.doi.org/10.1093/database/baad033 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Database Tool
Wable, Raghunandan
Nair, Achuth Suresh
Pappu, Anirudh
Pierre-Louis, Widnie
Abdelhalim, Habiba
Patel, Khushbu
Mendhe, Dinesh
Bolla, Shreyas
Mittal, Sahil
Ahmed, Zeeshan
Integrated ACMG-approved genes and ICD codes for the translational research and precision medicine
title Integrated ACMG-approved genes and ICD codes for the translational research and precision medicine
title_full Integrated ACMG-approved genes and ICD codes for the translational research and precision medicine
title_fullStr Integrated ACMG-approved genes and ICD codes for the translational research and precision medicine
title_full_unstemmed Integrated ACMG-approved genes and ICD codes for the translational research and precision medicine
title_short Integrated ACMG-approved genes and ICD codes for the translational research and precision medicine
title_sort integrated acmg-approved genes and icd codes for the translational research and precision medicine
topic Database Tool
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10191139/
https://www.ncbi.nlm.nih.gov/pubmed/37195695
http://dx.doi.org/10.1093/database/baad033
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