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KATZLDA: KATZ measure for the lncRNA-disease association prediction
Accumulating experimental studies have demonstrated important associations between alterations and dysregulations of lncRNAs and the development and progression of various complex human diseases. Developing effective computational models to integrate vast amount of heterogeneous biological data for...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2015
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4649494/ https://www.ncbi.nlm.nih.gov/pubmed/26577439 http://dx.doi.org/10.1038/srep16840 |
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author | Chen, Xing |
author_facet | Chen, Xing |
author_sort | Chen, Xing |
collection | PubMed |
description | Accumulating experimental studies have demonstrated important associations between alterations and dysregulations of lncRNAs and the development and progression of various complex human diseases. Developing effective computational models to integrate vast amount of heterogeneous biological data for the identification of potential disease-lncRNA associations has become a hot topic in the fields of human complex diseases and lncRNAs, which could benefit lncRNA biomarker detection for disease diagnosis, treatment, and prevention. Considering the limitations in previous computational methods, the model of KATZ measure for LncRNA-Disease Association prediction (KATZLDA) was developed to uncover potential lncRNA-disease associations by integrating known lncRNA-disease associations, lncRNA expression profiles, lncRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. KATZLDA could work for diseases without known related lncRNAs and lncRNAs without known associated diseases. KATZLDA obtained reliable AUCs of 7175, 0.7886, 0.7719 in the local and global leave-one-out cross validation and 5-fold cross validation, respectively, significantly improving previous classical methods. Furthermore, case studies of colon, gastric, and renal cancer were implemented and 60% of top 10 predictions have been confirmed by recent biological experiments. It is anticipated that KATZLDA could be an important resource with potential values for biomedical researches. |
format | Online Article Text |
id | pubmed-4649494 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-46494942015-11-23 KATZLDA: KATZ measure for the lncRNA-disease association prediction Chen, Xing Sci Rep Article Accumulating experimental studies have demonstrated important associations between alterations and dysregulations of lncRNAs and the development and progression of various complex human diseases. Developing effective computational models to integrate vast amount of heterogeneous biological data for the identification of potential disease-lncRNA associations has become a hot topic in the fields of human complex diseases and lncRNAs, which could benefit lncRNA biomarker detection for disease diagnosis, treatment, and prevention. Considering the limitations in previous computational methods, the model of KATZ measure for LncRNA-Disease Association prediction (KATZLDA) was developed to uncover potential lncRNA-disease associations by integrating known lncRNA-disease associations, lncRNA expression profiles, lncRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. KATZLDA could work for diseases without known related lncRNAs and lncRNAs without known associated diseases. KATZLDA obtained reliable AUCs of 7175, 0.7886, 0.7719 in the local and global leave-one-out cross validation and 5-fold cross validation, respectively, significantly improving previous classical methods. Furthermore, case studies of colon, gastric, and renal cancer were implemented and 60% of top 10 predictions have been confirmed by recent biological experiments. It is anticipated that KATZLDA could be an important resource with potential values for biomedical researches. Nature Publishing Group 2015-11-18 /pmc/articles/PMC4649494/ /pubmed/26577439 http://dx.doi.org/10.1038/srep16840 Text en Copyright © 2015, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Chen, Xing KATZLDA: KATZ measure for the lncRNA-disease association prediction |
title | KATZLDA: KATZ measure for the lncRNA-disease association prediction |
title_full | KATZLDA: KATZ measure for the lncRNA-disease association prediction |
title_fullStr | KATZLDA: KATZ measure for the lncRNA-disease association prediction |
title_full_unstemmed | KATZLDA: KATZ measure for the lncRNA-disease association prediction |
title_short | KATZLDA: KATZ measure for the lncRNA-disease association prediction |
title_sort | katzlda: katz measure for the lncrna-disease association prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4649494/ https://www.ncbi.nlm.nih.gov/pubmed/26577439 http://dx.doi.org/10.1038/srep16840 |
work_keys_str_mv | AT chenxing katzldakatzmeasureforthelncrnadiseaseassociationprediction |