Automatic Term Name Generation for Gene Ontology: Task and Dataset (2020)

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 reflect 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. Furthermore, we present a graph-based generative model that incorporates the relations between genes, words and terms for term name generation, which exhibits great advantages over the strong baselines.

## 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 @xcite . 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 protein function analysis @xcite and disease association prediction @xcite . a major concern in go is the go construction, including term discovery, naming and organization @xcite . in early studies, the terms are manually defined and organized by the experts in particular areas of biology, which is very labor-consuming and inefficient given the large volume of biological literature published every year @xcite . moreover, different experts may use different expressions to describe the same biological concept, causing an inconsistency problem in term naming. alias: insulin like growth factor binding protein 3 description: ...altering their interaction with cell surface receptors... recently, many researchers turn to develop automatic methods for go construction. dutkowski et al. @xcite 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. @xcite identified the gene cliques which were treated as a term in an integrated biological network. though these methods infer new go terms and their relationships based on the structured networks automatically @xcite @xcite , the new terms are still named manually by the experts, which is prone to the problems of inefficiency and inconsistency. furthermore, only the structure information in existing networks is utilized, while the genes' rich textual information that potentially describes the corresponding term has not well been studied. in order to obtain term names automatically to boost go construction, we propose a novel task that aims to generate term names based on the textual information of the related genes. an illustrative example of the task is shown in figure 1 . the genes igfbp3, ogfr and bap1 are annotated by the term with the id as go:0001558 and name as "regulation of cell growth". since there are some word overlaps between the term name and gene text (alias and description) by our observations, we aim to generate the term name based on the gene text. to facilitate the research, we first present a dataset for term name generation in go. then, we propose a graph-based generative model that incorporates the potential relations between genes, words and terms for term name generation. the experimental results indicate the effectiveness of our proposed model. the contributions of our work are threefold: (1) to the best of our knowledge, it is the first attempt to explore to generate term names for go automatically. (2) we present a large-scale dataset for term name generation based on various biological resources, which will help boost the research in bio-medicine and biology. (3) we conduct extensive experiments with in-depth analyses, which verify the effectiveness of our proposed model. ## dataset we build a large-scale dataset foot_0 for term name generation, which contains the go terms about homo sapiens (humankind). we collect the term id, term name and the corresponding genes' id from gene ontology consortium foot_1 . in addition, the gene alias and descriptions are crawled from genecards foot_2 , which contains the information from universal protein resource (uniprot) foot_3 . our dataset contains 18,092 samples in total. each sample contains a term id, term name and the related genes with alias and descriptions as demonstrated in figure 1 . the statistics and distributions about the dataset are shown in ## graph-based generative model the classical generative models such as seq2seq @xcite , hrnnlm @xcite and transformer @xcite only incorporate the sequential information of the source text for sentence generation, while the potential structure within the text is neglected. to alleviate this problem, we build a heterogeneous graph with the words, genes and terms as nodes, and adopt a graph-based generative model for term name generation. the overall architecture of our graph-based generative model is shown in figure 3 , which consists of two components: the gcn based encoder and the graph attention based decoder. ## conclusions and future work in this paper, we propose a novel task of automatic term name generation based on the gene text for go. we construct a large-scale dataset and provide the insights of this task. experimental results show that our proposed graph-based generative model is superior to other strong baselines by modeling the relations between genes, words and terms. in the future, we will explore how to utilize more knowledge to guide term name generation.