论文标题
Biont：使用多个生物医学本体论进行关系提取的深度学习
BiOnt: Deep Learning using Multiple Biomedical Ontologies for Relation Extraction
论文作者
论文摘要
成功的生物医学关系提取可以为研究人员和临床医生提供有关生物医学实体之间可能未知关联的证据，从而促进我们对这些实体及其固有机制的当前知识。大多数生物医学关系提取系统都不采用外部知识来源，例如领域特异性的本体。但是，使用深度学习方法以及生物医学本体学，最近已被证明可以有效地推进生物医学关系提取领域。为了进行关系提取，我们的深度学习系统Biont采用了四种类型的生物医学本体论，即基因本体论，人类表型本体论，人类疾病本体论和生物学兴趣的化学实体，分别是基因产物，表型，疾病和化学复合物。我们用三个代表生物医学实体的三种不同类型的关系的数据集测试了我们的系统。 Biont在F评分中实现了4.93个百分点的药物相互作用（DDI语料库），4.99个对表型基因关系（PGR语料库）的百分点和2.21个化学诱导疾病关系（BC5CDR）的百分点。支持该系统的代码可在https://github.com/lasigebiotm/biont上获得。
Successful biomedical relation extraction can provide evidence to researchers and clinicians about possible unknown associations between biomedical entities, advancing the current knowledge we have about those entities and their inherent mechanisms. Most biomedical relation extraction systems do not resort to external sources of knowledge, such as domain-specific ontologies. However, using deep learning methods, along with biomedical ontologies, has been recently shown to effectively advance the biomedical relation extraction field. To perform relation extraction, our deep learning system, BiOnt, employs four types of biomedical ontologies, namely, the Gene Ontology, the Human Phenotype Ontology, the Human Disease Ontology, and the Chemical Entities of Biological Interest, regarding gene-products, phenotypes, diseases, and chemical compounds, respectively. We tested our system with three data sets that represent three different types of relations of biomedical entities. BiOnt achieved, in F-score, an improvement of 4.93 percentage points for drug-drug interactions (DDI corpus), 4.99 percentage points for phenotype-gene relations (PGR corpus), and 2.21 percentage points for chemical-induced disease relations (BC5CDR corpus), relatively to the state-of-the-art. The code supporting this system is available at https://github.com/lasigeBioTM/BiOnt.
