Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS, USA.
Influenza Other Respir Viruses. 2011 May;5(Suppl 1):204-7.
Influenza antigenic cartography projects influenza antigens into a two or three dimensional map based on immunological datasets, such as hemagglutination inhibition and microneutralization assays. A robust antigenic cartography can facilitate influenza vaccine strain selection since the antigenic map can simplify data interpretation through intuitive antigenic map. However, antigenic cartography construction is not trivial due to the challenging features embedded in the immunological data, such as data incompleteness, high noises, and low reactors. To overcome these challenges, we developed a computational method, temporal Matrix Completion-Multidimensional Scaling (MC-MDS), by adapting the low rank MC concept from the movie recommendation system in Netflix and the MDS method from geographic cartography construction. The application on H3N2 and 2009 pandemic H1N1 influenza A viruses demonstrates that temporal MC-MDS is effective and efficient in constructing influenza antigenic cartography. The web sever is available at http://sysbio.cvm.msstate.edu/AntigenMap.
流感抗原绘图项目将流感抗原根据免疫数据集映射到二维或三维地图上，例如血凝抑制和微量中和测定。稳健的抗原绘图可以通过直观的抗原地图简化数据解释，从而促进流感疫苗株的选择。然而，由于免疫数据中嵌入了具有挑战性的特征，例如数据不完整、高噪声和低反应者，因此抗原绘图的构建并非易事。为了克服这些挑战，我们开发了一种计算方法，即时间矩阵补全-多维尺度分析（MC-MDS），通过从 Netflix 的电影推荐系统中适应低秩 MC 概念和从地理绘图构建中适应 MDS 方法。对 H3N2 和 2009 年大流行的 H1N1 流感 A 病毒的应用表明，时间 MC-MDS 有效地构建了流感抗原地图。网络服务器可在 http://sysbio.cvm.msstate.edu/AntigenMap 上获得。
