Assessment of HCV infection stage as recent or chronic using multi-parameter analysis and machine learning

IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences Pub Date : 2017-10-01 DOI:10.1109/ICCABS.2017.8114316

P. Baykal, Alexander Artyomenko, S. Ramachandran, Y. Khudyakov, A. Zelikovsky, P. Skums

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引用次数: 4

Abstract

Hepatitis C virus (HCV) usually establishes chronic infection, which is often asymptomatic at the early stages of disease. Unfortunately, no diagnostic criteria that can distinguish between recent and chronic HCV infections are available. Error-prone replication of HCV causes each patient to host a heterogeneous population of genetically related HCV variants. Therefore, it is usually supposed that intra-host HCV heterogeneity gradually increases over the course of infection. However, due to the complex nature of the structural development of HCV populations inside hosts being influenced by selective sweeps and negative selection during chronic infection [3][2], the accuracy of simple metrics for the assessment of genetic heterogeneity is insufficient for HCV infection staging.

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使用多参数分析和机器学习评估HCV感染分期为近期或慢性

丙型肝炎病毒(HCV)通常造成慢性感染，在疾病早期通常无症状。不幸的是，目前还没有能够区分近期和慢性丙型肝炎病毒感染的诊断标准。容易出错的丙型肝炎病毒复制导致每个患者携带一个遗传相关的异质人群的丙型肝炎病毒变异。因此，通常认为宿主内HCV异质性在感染过程中逐渐增加。然而，由于慢性感染期间宿主内HCV群体结构发育的复杂性受到选择性扫描和阴性选择的影响[3][2]，用于评估遗传异质性的简单指标的准确性不足以用于HCV感染分期。

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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IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences

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