P athway2 T ext: Dataset and Method for Biomedical Pathway Description Generation (2022)

Abstract: Biomedical pathways have been extensively used to characterize the mechanism of complex diseases. One essential step in biomedical pathway analysis is to curate the description of a pathway based on its graph structure and node features. Neural text generation could be a plausible technique to circumvent the tedious manual curation. In this paper, we propose a new dataset Pathway2Text, which contains 2,367 pairs of biomedical pathways and textual descriptions. All pathway graphs are experimentally derived or manually curated. All textual descriptions are written by domain experts. We form this problem as a Graph2Text task and propose a novel graph-based text generation approach k NN-Graph2Text, which explicitly exploited descriptions of similar graphs to generate new descriptions. We observed substantial improvement of our method on both Graph2Text and the reverse task of Text2Graph. We further illustrated how our dataset can be used as a novel benchmark for biomedical named entity recognition. Collectively, we envision our method will become an important benchmark for evaluating Graph2Text methods and advance biomedical research for complex diseases.

## introduction many complex diseases, such as cancer and neurodegenerative disorders, are driven by reactions among a combination of genes and metabolites instead of one single gene @xcite . these reactions, which are formally referred to as pathways @xcite @xcite , are represented as a heterogeneous graph (figure 1 ). each node in this graph is a biomedical entity, such as gene, chemical or metabolite. each edge is a specific biomedical reaction. using natural language to describe this pathway graph is of great importance for scientific communication and further promotes applications in complex disease research @xcite . to date, these descriptions are almost entirely curated manually by domain experts, thus substantially slowing down downstream biomedical applications @xcite . neural text generation has shown promising results in many applications @xcite @xcite @xcite . among them, graph-to-text (graph2text) generation, such as amr-to-text @xcite @xcite , and knowledge-graph-to-text @xcite , is most similar to pathway description generation. therefore, we hypothesize that neural text generation could also be a solution here. to fill in the gap, we first propose a novel biomedical pathway description dataset path-way2text, which contains 2,367 pairs of pathway and description. each description is written by domain experts, describing the function and property of this pathway. in contrast to many other graph2text datasets @xcite that use automatic approach to extract the graph from the text, pathways in our dataset are all experimentally measured or manually curated, presenting a high-quality structured data corresponding to the textual description. to the best of our knowledge, pathway2text is the first large-scale dataset studying the problem of biomedical pathway description generation. one unique feature of our dataset is the rich textual information on each node in the graph. specif- succinic acid ... protein succinylation prolyl 4-hydroxylase subunit alpha-3 hypoxia-inducible factor 1-alpha interleukin-1 beta ## task description we aim to generate the textual description for a given biomedical pathway graph and generate the biomedical pathway graph from a given textual description. one unique property of the graphs in our dataset is the rich node features @xmath0, t, d}. in particular, each node v is associated with three features g v , t v , and graph2text. the input of this task is a graph g. all node features are observed on this graph. the output is the description text s for this graph. text2graph link prediction. this task aims to multi-head attention gnn transformer knn biobert gnn fix graph embedding node embedding description embedding description of similar graphs final generation transformer succinic acid, or it succinate, can make mitochondria as expression ... succinic acid, or it succinate, can make mitochondria as expression ... succinic acid, or it succinate, can make mitochondria as expression ... succinic acid, or it succinate, can make mitochondria as expression ... succinic acid, or it succinate, can make mitochondria as expression ... succinic acid, or its anion succinate, can leave the mitochondria an.. succinic acid, or its anion succinate, can leave the mitochondria and can directly inh... ... ... ... the krebs cycle, also known as the citric acid cycle (cac) or tricarboxylic acid cycle (tca cycle) occurs in the mitochondria, and it involves the oxidation of acetyl-coa from glycolysis to ... step 1 step 2 description in training data succinic acid, or its anion succinate, can leave the mitochondria and can directly inh... in the first step, we learnt a representation for each graph by projecting graphs to descriptions. in the second step, we find similar graphs for a test graph and jointly use descriptions of similar graphs and node embeddings of the test graph to generate the final description. text2graph node classification. this task aims to classify each test node into a specific node type in graph g. we split nodes in g into training nodes and test nodes. for training nodes, we observed all node features f , including textual label, textual description and node type, whereas none of these features is observed for the test node. we also observed the graph description s for g. instead of predicting the node type, we aim at predicting the specific textual label, which is a more challenging task. we form this problem as a node classification task instead of textual generation. ## application to named entity recognition named entity recognition (ner) is essential in detecting chemicals, genes, and diseases from biomedical text @xcite @xcite , and further facilitating downstream bionlp applications, such as relation extraction @xcite . a major bottleneck in ner is the lack of curated benchmarks since such curation often requires substantial domain expertise. our dataset path2waytext can be used as a novel curated benchmark for ner. specifically, we used the graph description as the sentences that one wants to perform ner. we then obtained the ground truth entity type of phrases in these sentences according to their curated node types in the graph. since the graphs, including all node types, are curated by domain experts, such node types can be used as the ground truth entity types for ner. here, we focused on two most frequent entity types in our dataset: protein and chemical. we noticed that some phrases in the graph description sentences might also be a protein or chemical, even though they were not curated in the graph. we excluded such phrases in the evaluation in order to maintain the quality of our ner benchmark. to this end, we obtained the graph-based curation of 8,779 protein entities and 1,621 chemical entities, offering a good complementary to existing biomedical ner datasets @xcite @xcite @xcite . to further investigate the performance of our novel ner datasets, we tested a few state-of-the-art biomedical ner methods, including bern @xcite , collabonet @xcite , multi-bioner @xcite , and neuroner @xcite . we observed that neuroner obtained the best performance on protein and multi-bioner achieved the best performance on chemical (figure 5c ). moreover, existing approaches only consider the graph description sentences when labelling entity types. in addition to graph description, our dataset also contains the corresponding graph structure, which has been shown to be critical in graph description generation in our experiments. therefore, we hypothesize that graph structure might be also helpful in ner, and envision our dataset to be an important resource for benchmarking graph-based ner methods @xcite @xcite . ## related work graph2text, which aims at generating a textual description for a structured graph, has attracted attentions in different applications. existing graph2text datasets aims to generate text from rdf data @xcite , knowledge graph @xcite @xcite @xcite , street view map @xcite , abstract meaning representation (amr) @xcite @xcite @xcite @xcite @xcite @xcite , terminology ontology @xcite and graph-transduction grammars @xcite @xcite . our dataset is the first graph2text dataset that focuses on biomedical pathway generation. in addition, our dataset has more complicated node features than many existing graph2text datasets, where each node in our dataset has a node type, a concise textual label and a detailed textual description. text2graph can be viewed as an information extraction task, which aims at mining structured knowledge from free text. the datasets that are more relevant to our task could be generating a knowledge graph from long document @xcite @xcite . many of these existing datasets use automatic annotation to extract the graph information from corpus @xcite , which might introduce bias from the extraction method. in contrast, graphs in our dataset are either experimentally derived or manually curated, presenting a high-quality complementary to existing text2graph datasets. ## conclusion and future work we have presented a novel dataset pathway2text for biomedical pathway description generation. our dataset contains 2,367 pairs of curated pathway and its associated description. to generated description for biomedical pathways, we have proposed a knn-graph2text approach, which utilizes neighbor's description to enhance the text generation. we have extensively evaluated our method and observed substantial improvement in comparison to conventional graph neural network archi-tectures. furthermore, we have investigated the reverse task of text2graph and illustrated how our dataset can serve as a novel benchmark for biomedical ner. in addition to graph2text, text2graph and ner, our dataset can also be used to investigate other important applications. for example, our dataset can be used as a relation extraction benchmark by regarding graph descriptions as sentences and graph edge types as the ground truth relation type. we can also use our dataset to study other graphbased tasks, such as generating node description given the graph structure and the graph description. another interesting application is to identify the importance of each node in the graph, which has important applications in recommender system and social media. the order of mentions of each node in the graph description can be used to evaluate the node importance since the graph description often starts from the most important node. from a methodological perspective, we plan to develop semi-supervised approaches to leverage many other biomedical pathways that currently do not have curated description. for example, we can train a graph transformer (cai and lam, 2020) on these unlabelled pathways and then finetune the model on pathways with graph description. we also want to explore other geometric embedding methods, such as hyperbolic embedding @xcite and spherical embedding @xcite , since biomedical pathways often form a hierarchical structure. more importantly, our dataset could also open up new venues in biomedical research. any computational biology tools that utilize biomedical pathways as features in their pipeline can exploit the graph description as additional features. for biomedical pathways that do not have the corresponding description, one can use the description generated by our knn-graph2text as the feature. we envision this will substantially advance a wide range of biomedical research that involves pathway analysis, and our dataset will introduce other new text generation tools developed in the nlp community to broader audience in biomedicine.