Division of Humanities, CheongJu University, CheongJu, Korea.
Department of Library and Information Science, Yonsei University, Seoul, Korea.
PLoS One. 2019 Apr 24;14(4):e0215313. doi: 10.1371/journal.pone.0215313. eCollection 2019.
In 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.
In 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.
The 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.
在基于文献的发现中，已经基于 Swanson 提出的 ABC 模型进行了大量研究。ABC 模型假设，通过同时出现在两个文档集中的 B 实体，可以在从文档集 1 中提取的实体 A 和从文档集 2 中提取的实体 C 之间建立有意义的关系。ABC 模型的结果是实体 A、B 和 C 之间的关系，称为路径。一条路径允许假设实体 A 和实体 C 之间的关系，或者有助于发现实体 B 作为实体 A 和实体 C 之间关系的新证据。ABC 模型的共现方法是通过创建各种路径来自动生成假设的知名方法。然而，基于共现的 ABC 模型存在一个局限性，即不考虑生物背景。它只关注共同出现在两个实体之间关系中的 B 实体的匹配。因此，基于共现的 ABC 模型提取的路径往往包含很多不相关的路径，这意味着需要专家验证。
为了克服基于共现的 ABC 模型的局限性，我们提出了一种基于上下文的方法来连接一个实体关系到另一个实体关系，使用生物背景修改 ABC 模型。在这项研究中，我们定义了四个生物背景元素：细胞、药物、疾病和生物体。基于这些生物背景，我们提出了两个扩展的 ABC 模型：基于上下文的 ABC 模型和基于上下文分配的 ABC 模型。为了衡量这两个提出的模型的性能，我们检查了已知关系“APOE-MAPT”和“FUS-TARDBP”之间 B 实体的相关性。每个关系都意味着与神经退行性疾病相关的蛋白质之间的相互作用。APOE 与 MAPT 之间的相互作用已知在阿尔茨海默病中发挥关键作用，因为 APOE 影响 tau 介导的神经退行性变。已经表明 FUS 和 TARDBP 的突变与肌萎缩侧索硬化症（ALS）有关，这是一种由神经元细胞死亡引起的运动神经元疾病。使用这两个关系，我们将这两种模型与基于共现的 ABC 模型进行了比较。
基于共现的 ABC 模型的 B 实体的精度分别为“APOE-MAPT”的 27.1%和“FUS-TARDBP”的 22.1%。在基于上下文的 ABC 模型中，“APOE-MAPT”的 B 实体提取精度为 71.4%，“FUS-TARDBP”为 77.9%。基于上下文分配的 ABC 模型分别为这两个关系实现了 89%和 97.5%的精度。这两种提出的模型的精度都高于基于共现的 ABC 模型。
