Biomedical Informatics Lab, School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.
BMC Genomics. 2019 Dec 24;20(Suppl 9):973. doi: 10.1186/s12864-019-6295-8.
Influenza A virus (IAV) poses threats to human health and life. Many individual studies have been carried out in mice to uncover the viral factors responsible for the virulence of IAV infections. Nonetheless, a single study may not provide enough confident about virulence factors, hence combining several studies for a meta-analysis is desired to provide better views. For this, we documented more than 500 records of IAV infections in mice, whose viral proteins could be retrieved and the mouse lethal dose 50 or alternatively, weight loss and/or survival data, was/were available for virulence classification.
IAV virulence models were learned from various datasets containing aligned IAV proteins and the corresponding two virulence classes (avirulent and virulent) or three virulence classes (low, intermediate and high virulence). Three proven rule-based learning approaches, i.e., OneR, JRip and PART, and additionally random forest were used for modelling. PART models achieved the best performance, with moderate average model accuracies ranged from 65.0 to 84.4% and from 54.0 to 66.6% for the two-class and three-class problems, respectively. PART models were comparable to or even better than random forest models and should be preferred based on the Occam's razor principle. Interestingly, the average accuracy of the models was improved when host information was taken into account. For model interpretation, we observed that although many sites in HA were highly correlated with virulence, PART models based on sites in PB2 could compete against and were often better than PART models based on sites in HA. Moreover, PART had a high preference to include sites in PB2 when models were learned from datasets containing the concatenated alignments of all IAV proteins. Several sites with a known contribution to virulence were found as the top protein sites, and site pairs that may synergistically influence virulence were also uncovered.
Modelling IAV virulence is a challenging problem. Rule-based models generated using viral proteins are useful for its advantage in interpretation, but only achieve moderate performance. Development of more advanced approaches that learn models from features extracted from both viral and host proteins shall be considered for future works.
甲型流感病毒（IAV）对人类健康和生命构成威胁。许多针对小鼠的个体研究已经揭示了导致 IAV 感染毒力的病毒因子。然而，单一研究可能无法提供足够的关于毒力因子的置信度，因此需要结合多个研究进行荟萃分析，以提供更好的视角。为此，我们记录了超过 500 份 IAV 在小鼠中的感染记录，这些记录可以检索到病毒蛋白，并且可以获得半数致死量（LD50）或体重减轻和/或生存数据，用于毒力分类。
我们从包含对齐 IAV 蛋白和相应的两个毒力类别（弱毒和强毒）或三个毒力类别（低、中和高毒力）的多个数据集中学到了 IAV 毒力模型。我们使用了三种经过验证的基于规则的学习方法，即 OneR、JRip 和 PART，以及随机森林来进行建模。PART 模型的性能最佳，其平均模型准确率适中，分别为 65.0%至 84.4%和 54.0%至 66.6%，用于二分类和三分类问题。PART 模型与随机森林模型相当，甚至更好，并且应该基于奥卡姆剃刀原理而被优先选择。有趣的是，当考虑宿主信息时，模型的平均准确性得到了提高。对于模型解释，我们观察到尽管 HA 中的许多位点与毒力高度相关，但基于 PB2 中的位点的 PART 模型可以与基于 HA 中的位点的 PART 模型竞争，并且通常优于后者。此外，当从包含所有 IAV 蛋白的串联对齐数据集中学到模型时，PART 更倾向于包含 PB2 中的位点。发现了一些具有已知毒力贡献的位点作为蛋白质的顶级位点，并且还发现了可能协同影响毒力的位点对。
IAV 毒力建模是一个具有挑战性的问题。使用病毒蛋白生成的基于规则的模型在解释方面具有优势，但仅达到中等性能。未来的工作应该考虑开发更先进的方法，从病毒和宿主蛋白中提取特征来学习模型。
