A context-based ABC model for literature-based discovery

BackgroundIn the literature-based discovery, considerable research has been done based on the ABC model developed by Swanson. ABC model hypothesizes that there is a meaningful relation between entity A extracted from document set 1 and entity C extracted from document set 2 through B entities that appear commonly in both document sets. The results of ABC model are relations among entity A, B, and C, which is referred as paths. A path allows for hypothesizing the relationship between entity A and entity C, or helps discover entity B as a new evidence for the relationship between entity A and entity C. The co-occurrence based approach of ABC model is a well-known approach to automatic hypothesis generation by creating various paths. However, the co-occurrence based ABC model has a limitation, in that biological context is not considered. It focuses only on matching of B entity which commonly appears in relation between two entities. Therefore, the paths extracted by the co-occurrence based ABC model tend to include a lot of irrelevant paths, meaning that expert verification is essential.MethodsIn order to overcome this limitation of the co-occurrence based ABC model, we propose a context-based approach to connecting one entity relation to another, modifying the ABC model using biological contexts. In this study, we defined four biological context elements: cell, drug, disease, and organism. Based on these biological context, we propose two extended ABC models: a context-based ABC model and a context-assignment-based ABC model. In order to measure the performance of the both proposed models, we examined the relevance of the B entities between the well-known relations “APOE–MAPT” as well as “FUS–TARDBP”. Each relation means interaction between neurodegenerative disease associated with proteins. The interaction between APOE and MAPT is known to play a crucial role in Alzheimer’s disease as APOE affects tau-mediated neurodegeneration. It has been shown that mutation in FUS and TARDBP are associated with amyotrophic lateral sclerosis(ALS), a motor neuron disease by leading to neuronal cell death. Using these two relations, we compared both of proposed models to co-occurrence based ABC model.ResultsThe precision of B entities by co-occurrence based ABC model was 27.1% for “APOE–MAPT” and 22.1% for “FUS–TARDBP”, respectively. In context-based ABC model, precision of extracted B entities was 71.4% for “APOE–MAPT”, and 77.9% for “FUS–TARDBP”. Context-assignment based ABC model achieved 89% and 97.5% precision for the two relations, respectively. Both proposed models achieved a higher precision than co-occurrence-based ABC model.### 背景 在基于文献的发现（literature-based discovery）领域中，已有大量研究基于斯旺森（Swanson）提出的ABC模型展开。ABC模型假设：从文档集1中提取的实体A与从文档集2中提取的实体C之间，可通过两类文档集中共通的实体B建立有意义的关联。ABC模型的输出结果为实体A、B、C之间的关联关系，此类关系被称为路径（path）。单条路径既可以用于推测实体A与实体C之间的潜在关联，也可帮助发现实体B，将其作为实体A与C之间关联的新佐证。基于共现（co-occurrence）的ABC模型是一种通过生成各类路径实现自动假说生成的经典方法。然而，该基于共现的ABC模型存在局限性：未考虑生物学上下文（biological context），仅聚焦于匹配两类实体关联中共通的B实体。因此，基于共现的ABC模型所提取的路径往往包含大量无关路径，这意味着必须进行专家人工验证。 ### 方法 为克服基于共现的ABC模型的上述局限，我们提出一种基于上下文的实体关联连接方法，利用生物学上下文对ABC模型进行改进。本研究定义了四类生物学上下文元素：细胞（cell）、药物（drug）、疾病（disease）与生物体（organism）。基于这些生物学上下文元素，我们提出两种扩展ABC模型：基于上下文的ABC模型，以及基于上下文赋值的ABC模型。为评估所提两种模型的性能，我们针对两组已知关联"APOE–MAPT"与"FUS–TARDBP"，检验了其中B实体的相关性。上述两组关联分别指代与蛋白质相关的神经退行性疾病中，对应蛋白质之间的相互作用。已知APOE与MAPT的相互作用在阿尔茨海默病中发挥关键作用，因为载脂蛋白E（APOE）会影响tau蛋白介导的神经退行性过程。已有研究表明，FUS与TARDBP的突变与肌萎缩侧索硬化症（amyotrophic lateral sclerosis, ALS，一种运动神经元疾病）相关，该疾病可通过引发神经元细胞死亡导致病症进展。我们利用这两组关联，将所提出的两种模型与基于共现的ABC模型进行了对比。 ### 结果 基于共现的ABC模型所得到的B实体精确率，在"APOE–MAPT"组中为27.1%，在"FUS–TARDBP"组中为22.1%。在基于上下文的ABC模型中，所提取B实体的精确率分别为71.4%（对应"APOE–MAPT"组）与77.9%（对应"FUS–TARDBP"组）。基于上下文赋值的ABC模型在两组关联中的精确率分别达到89%与97.5%。所提出的两种模型的精确率均高于基于共现的ABC模型。