Bayesian Analysis of Complex Mutations in HBV, HCV, and HIV Studies
In this article, we aim to provide a thorough review of the Bayesian-inference-based methods applied to Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), and Human Immunodeficiency Virus (HIV) studies with a focus on the detection of the viral mutations and various problems which are correlated to t...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2019-09-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2019.9020005 |
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| author | Bing Liu Shishi Feng Xuan Guo Jing Zhang |
| author_facet | Bing Liu Shishi Feng Xuan Guo Jing Zhang |
| author_sort | Bing Liu |
| collection | DOAJ |
| description | In this article, we aim to provide a thorough review of the Bayesian-inference-based methods applied to Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), and Human Immunodeficiency Virus (HIV) studies with a focus on the detection of the viral mutations and various problems which are correlated to these mutations. It is particularly difficult to detect and interpret these interacting mutation patterns, but by using Bayesian statistical modeling, it provides a groundbreaking opportunity to solve these problems. Here we summarize Bayesian-based statistical approaches, including the Bayesian Variable Partition (BVP) model, Bayesian Network (BN), and the Recursive Model Selection (RMS) procedure, which are designed to detect the mutations and to make further inferences to the comprehensive dependence structure among the interactions. BVP, BN, and RMS in which Markov Chain Monte Carlo (MCMC) methods are used have been widely applied in HBV, HCV, and HIV studies in the recent years. We also provide a summary of the Bayesian methods’ applications toward these viruses’ studies, where several important and useful results have been discovered. We envisage the applications of more modified Bayesian methods to other infectious diseases and cancer cells that will be following with critical medical results before long. |
| format | Article |
| id | doaj-art-ab43c7bfe12d4607bd2e68a5408af1e7 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2019-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-ab43c7bfe12d4607bd2e68a5408af1e72025-02-02T23:47:56ZengTsinghua University PressBig Data Mining and Analytics2096-06542019-09-012314515810.26599/BDMA.2019.9020005Bayesian Analysis of Complex Mutations in HBV, HCV, and HIV StudiesBing Liu0Shishi Feng1Xuan Guo2Jing Zhang3<institution content-type="dept">Department of Mathematics and Statistics</institution>, <institution>Georgia State University</institution>, <city>Atlanta</city>, <state>GA</state> <postal-code>30303</postal-code>, <country>USA</country>.<institution content-type="dept">Department of Mathematics and Statistics</institution>, <institution>Georgia State University</institution>, <city>Atlanta</city>, <state>GA</state> <postal-code>30303</postal-code>, <country>USA</country>.<institution content-type="dept">Department of Computer Science and Engineering</institution>, <institution>University of North Texas</institution>, <city>Denton</city>, <state>TX</state> <postal-code>76203</postal-code>, <country>USA</country>.<institution content-type="dept">Department of Mathematics and Statistics</institution>, <institution>Georgia State University</institution>, <city>Atlanta</city>, <state>GA</state> <postal-code>30303</postal-code>, <country>USA</country>.In this article, we aim to provide a thorough review of the Bayesian-inference-based methods applied to Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), and Human Immunodeficiency Virus (HIV) studies with a focus on the detection of the viral mutations and various problems which are correlated to these mutations. It is particularly difficult to detect and interpret these interacting mutation patterns, but by using Bayesian statistical modeling, it provides a groundbreaking opportunity to solve these problems. Here we summarize Bayesian-based statistical approaches, including the Bayesian Variable Partition (BVP) model, Bayesian Network (BN), and the Recursive Model Selection (RMS) procedure, which are designed to detect the mutations and to make further inferences to the comprehensive dependence structure among the interactions. BVP, BN, and RMS in which Markov Chain Monte Carlo (MCMC) methods are used have been widely applied in HBV, HCV, and HIV studies in the recent years. We also provide a summary of the Bayesian methods’ applications toward these viruses’ studies, where several important and useful results have been discovered. We envisage the applications of more modified Bayesian methods to other infectious diseases and cancer cells that will be following with critical medical results before long.https://www.sciopen.com/article/10.26599/BDMA.2019.9020005bayesian analysishepatitis b virus (hbv)hepatitis c virus (hcv)human immunodeficiency virus (hiv)complex mutationsmarkov chain monte carlo |
| spellingShingle | Bing Liu Shishi Feng Xuan Guo Jing Zhang Bayesian Analysis of Complex Mutations in HBV, HCV, and HIV Studies Big Data Mining and Analytics bayesian analysis hepatitis b virus (hbv) hepatitis c virus (hcv) human immunodeficiency virus (hiv) complex mutations markov chain monte carlo |
| title | Bayesian Analysis of Complex Mutations in HBV, HCV, and HIV Studies |
| title_full | Bayesian Analysis of Complex Mutations in HBV, HCV, and HIV Studies |
| title_fullStr | Bayesian Analysis of Complex Mutations in HBV, HCV, and HIV Studies |
| title_full_unstemmed | Bayesian Analysis of Complex Mutations in HBV, HCV, and HIV Studies |
| title_short | Bayesian Analysis of Complex Mutations in HBV, HCV, and HIV Studies |
| title_sort | bayesian analysis of complex mutations in hbv hcv and hiv studies |
| topic | bayesian analysis hepatitis b virus (hbv) hepatitis c virus (hcv) human immunodeficiency virus (hiv) complex mutations markov chain monte carlo |
| url | https://www.sciopen.com/article/10.26599/BDMA.2019.9020005 |
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