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HIV-1 tropism prediction by the XGboost and HMM methods
Human Immunodeficiency Virus 1 (HIV-1) co-receptor usage, called tropism, is associated with disease progression towards AIDS. Furthermore, the recently developed and developing drugs against co-receptors CCR5 or CXCR4 open a new thought for HIV-1 therapy. Thus, knowledge about tropism is critical f...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6620319/ https://www.ncbi.nlm.nih.gov/pubmed/31292462 http://dx.doi.org/10.1038/s41598-019-46420-4 |
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author | Chen, Xiang Wang, Zhi-Xin Pan, Xian-Ming |
author_facet | Chen, Xiang Wang, Zhi-Xin Pan, Xian-Ming |
author_sort | Chen, Xiang |
collection | PubMed |
description | Human Immunodeficiency Virus 1 (HIV-1) co-receptor usage, called tropism, is associated with disease progression towards AIDS. Furthermore, the recently developed and developing drugs against co-receptors CCR5 or CXCR4 open a new thought for HIV-1 therapy. Thus, knowledge about tropism is critical for illness diagnosis and regimen prescription. To improve tropism prediction accuracy, we developed two novel methods, the extreme gradient boosting based XGBpred and the hidden Markov model based HMMpred. Both XGBpred and HMMpred achieved higher specificities (72.56% and 72.09%) than the state-of-the-art methods Geno2pheno (61.6%) and G2p_str (68.60%) in a 10-fold cross validation test at the same sensitivity of 93.73%. Moreover, XGBpred had more outstanding performances (with AUCs 0.9483, 0.9464) than HMMpred (0.8829, 0.8774) on the Hivcopred and Newdb (created in this work) datasets containing larger proportions of hard-to-predict dual tropic samples in the X4-using tropic samples. Therefore, we recommend the use of our novel method XGBpred to predict tropism. The two methods and datasets are available via http://spg.med.tsinghua.edu.cn:23334/XGBpred/. In addition, our models identified that positions 5, 11, 13, 18, 22, 24, and 25 were correlated with HIV-1 tropism. |
format | Online Article Text |
id | pubmed-6620319 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-66203192019-07-18 HIV-1 tropism prediction by the XGboost and HMM methods Chen, Xiang Wang, Zhi-Xin Pan, Xian-Ming Sci Rep Article Human Immunodeficiency Virus 1 (HIV-1) co-receptor usage, called tropism, is associated with disease progression towards AIDS. Furthermore, the recently developed and developing drugs against co-receptors CCR5 or CXCR4 open a new thought for HIV-1 therapy. Thus, knowledge about tropism is critical for illness diagnosis and regimen prescription. To improve tropism prediction accuracy, we developed two novel methods, the extreme gradient boosting based XGBpred and the hidden Markov model based HMMpred. Both XGBpred and HMMpred achieved higher specificities (72.56% and 72.09%) than the state-of-the-art methods Geno2pheno (61.6%) and G2p_str (68.60%) in a 10-fold cross validation test at the same sensitivity of 93.73%. Moreover, XGBpred had more outstanding performances (with AUCs 0.9483, 0.9464) than HMMpred (0.8829, 0.8774) on the Hivcopred and Newdb (created in this work) datasets containing larger proportions of hard-to-predict dual tropic samples in the X4-using tropic samples. Therefore, we recommend the use of our novel method XGBpred to predict tropism. The two methods and datasets are available via http://spg.med.tsinghua.edu.cn:23334/XGBpred/. In addition, our models identified that positions 5, 11, 13, 18, 22, 24, and 25 were correlated with HIV-1 tropism. Nature Publishing Group UK 2019-07-10 /pmc/articles/PMC6620319/ /pubmed/31292462 http://dx.doi.org/10.1038/s41598-019-46420-4 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Chen, Xiang Wang, Zhi-Xin Pan, Xian-Ming HIV-1 tropism prediction by the XGboost and HMM methods |
title | HIV-1 tropism prediction by the XGboost and HMM methods |
title_full | HIV-1 tropism prediction by the XGboost and HMM methods |
title_fullStr | HIV-1 tropism prediction by the XGboost and HMM methods |
title_full_unstemmed | HIV-1 tropism prediction by the XGboost and HMM methods |
title_short | HIV-1 tropism prediction by the XGboost and HMM methods |
title_sort | hiv-1 tropism prediction by the xgboost and hmm methods |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6620319/ https://www.ncbi.nlm.nih.gov/pubmed/31292462 http://dx.doi.org/10.1038/s41598-019-46420-4 |
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