论文标题
用于预测途径级别亚细胞定位的图形算法
Graph algorithms for predicting subcellular localization at the pathway level
论文作者
论文摘要
蛋白质亚细胞定位是正常细胞过程和疾病的重要因素。尽管许多蛋白质定位资源将其视为静态，但蛋白质定位是动态的，受生物环境的影响很大。生物途径是代表特定生物学环境的图形，可以从大规模数据中推断出来。我们开发了图算法，以预测生物途径中所有相互作用的定位作为边缘标记任务。我们比较了各种模型，包括图形神经网络，概率图形模型和判别性分类器，以预测策划途径数据库的本地化注释。我们还进行了一个案例研究，在该案例研究中，我们可以构建生物学途径并预测患有病毒感染的人成纤维细胞的局部性。途径定位预测是将公开可用的本地化数据整合到大规模生物学数据分析中的一种有前途的方法。
Protein subcellular localization is an important factor in normal cellular processes and disease. While many protein localization resources treat it as static, protein localization is dynamic and heavily influenced by biological context. Biological pathways are graphs that represent a specific biological context and can be inferred from large-scale data. We develop graph algorithms to predict the localization of all interactions in a biological pathway as an edge-labeling task. We compare a variety of models including graph neural networks, probabilistic graphical models, and discriminative classifiers for predicting localization annotations from curated pathway databases. We also perform a case study where we construct biological pathways and predict localizations of human fibroblasts undergoing viral infection. Pathway localization prediction is a promising approach for integrating publicly available localization data into the analysis of large-scale biological data.
