Automatic Term Name Generation for Gene Ontology: Task and Dataset

Automatic Term Name Generation for Gene Ontology: Task and Dataset Yanjian Zhang1, Qin Chen1∗, Yiteng Zhang1, Yixu Gao1, Zhongyu Wei15, Jiajie Peng2, Zengfeng Huang1, Weijian Sun3, Xuanjing Huang4 1School of Data Science, Fudan University 2School of Computer Science, Northwestern Polytechnical University 3Huawei Technologies Co., Ltd 4School of Computer Science, Fudan University 5Research Institute of Intelligent and Complex Systems, Fudan University {yanjianzhang16,qin chen,yitengzhang19,yxgao19,zywei,huangzf,xjhuang}@fudan.edu.cn jiajiepeng@nwpu.edu.cn sunweijian@huawei.com Abstract Terms contained in Gene Ontology (GO) have been widely used in biology and bio-medicine. Most previous research focuses on inferring new GO terms, while the term names that re- flect the gene function are still named by the experts. To fill this gap, we propose a novel task, namely term name generation for GO, and build a large-scale benchmark dataset. Fur- thermore, we present a graph-based generative model that incorporates the relations between genes, words and terms for term name genera- tion, which exhibits great advantages over the strong baselines. 1 Introduction and Related Work Gene Ontology (GO) is a widely-used biological ontology, which contains a large number of terms to describe the gene function in three aspects, namely molecular function, biological process and cellular component (Consortium, 2015, 2016). The terms are organized hierarchically like a tree, and can be used to annotate the genes as demonstrated in Figure 1. GO has been extensively studied in the research community of bio-medicine and biology for its great value in many applications, such as pro- tein function analysis (Cho et al., 2016) and disease association prediction (Menche et al., 2015). A major concern in GO is the GO construction, including term discovery, naming and organiza- tion (Mazandu et al., 2017; Koopmans et al., 2019). In early studies, the terms are manually defined and organized by the experts in particular areas of biol- ogy, which is very labor-consuming and inefficient given the large volume of biological literature pub- lished every year (Tomczak et al., 2018). Moreover, different experts may use different expressions to describe the same biological concept, causing an inconsistency problem in term naming. ∗Corresponding author Alias: Insulin like growth factor binding protein 3 Description: ...altering their interaction with cell surface receptors... Alias: Opioid growth factor receptor Description: ...a receptor for opioid growth factor... Alias: Brca1 associated protein 1 Description: ...the enzyme may be involved in regulation of cell cycle... … … Term Gene IGFBP3 OGFR BAP1 GO: 0001558 Regulation of cell growth Figure 1: A term named “Regulation of cell growth” and the related genes with aliases and descriptions. Recently, many researchers turn to develop au- tomatic methods for GO construction. Dutkowski et al. (Dutkowski et al., 2013) proposed a Network- eXtracted Ontology (NeXO), which clustered genes hierarchically based on their connections in the molecular networks, and recovered around 40% of the terms according to the alignment between NeXO and GO. In order to further improve the performance, Kramer et al. (Kramer et al., 2014) identified the gene cliques which were treated as a term in an integrated biological network. Though these methods infer new GO …

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