丙型肝炎病毒基因组位点之间的协调进化与宿主因子和对干扰素的耐药性有关。

Q2 Medicine In Silico Biology Pub Date : 2011-01-01 DOI:10.3233/ISB-2012-0456

James Lara, John E Tavis, Maureen J Donlin, William M Lee, He-Jun Yuan, Brian L Pearlman, Gilberto Vaughan, Joseph C Forbi, Guo-Liang Xia, Yury E Khudyakov

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引用次数: 16

摘要

使用贝叶斯网络(BN)、线性投影(LP)和自组织树(SOT)模型形式的机器学习方法来探索HCV基因组HVR1和NS5a区域内多态性位点、宿主人口统计学因素(种族、性别和年龄)以及对干扰素(IFN)和利巴韦林(RBV)联合治疗的反应之间的关系。BN模型预测治疗结果、性别和种族的准确率分别为90%、90%和88.9%。LP和SOT模型有力地证实了HVR1和NS5A结构与BN确定的治疗反应和人口统计学宿主因素之间的关联。这些数据表明HCV进化的宿主特异性，并建议应用这些模型来预测IFN/RBV治疗的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Coordinated evolution among hepatitis C virus genomic sites is coupled to host factors and resistance to interferon.

Machine-learning methods in the form of Bayesian networks (BN), linear projection (LP) and self-organizing tree (SOT) models were used to explore association among polymorphic sites within the HVR1 and NS5a regions of the HCV genome, host demographic factors (ethnicity, gender and age) and response to the combined interferon (IFN) and ribavirin (RBV) therapy. The BN models predicted therapy outcomes, gender and ethnicity with accuracy of 90%, 90% and 88.9%, respectively. The LP and SOT models strongly confirmed associations of the HVR1 and NS5A structures with response to therapy and demographic host factors identified by BN. The data indicate host specificity of HCV evolution and suggest the application of these models to predict outcomes of IFN/RBV therapy.

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来源期刊

In Silico Biology Computer Science-Computational Theory and Mathematics

CiteScore

2.20

自引率

0.00%

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期刊介绍： The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.