Vapur: A Search Engine to Find Related Protein - Compound Pairs in COVID -19 Literature (2020)

Abstract: Coronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an intense scientific effort from different domains. The resulting publications created a huge text collection in which finding the studies related to a biomolecule of interest is challenging for general purpose search engines because the publications are rich in domain specific terminology. Here, we present Vapur: an online COVID-19 search engine specifically designed to find related protein - chemical pairs. Vapur is empowered with a relation-oriented inverted index that is able to retrieve and group studies for a query biomolecule with respect to its related entities. The inverted index of Vapur is automatically created with a BioNLP pipeline and integrated with an online user interface. The online interface is designed for the smooth traversal of the current literature by domain researchers and is publicly available at https://tabilab.cmpe.boun.edu.tr/vapur/ .

## introduction coronavirus disease of 2019 (covid-19) outbreak had severe impacts on human health all around the world since december 2019, but also triggered an exceptional amount of scientific work. as of september 2020, pubmed recorded over 45k articles @xcite related to covid-19 since its inception, including works on diagnosis @xcite , drug repurposing @xcite , and text-mining @xcite . as the body of literature keeps growing in the form of unstructured text, it also becomes more and more challenging for researchers to find the relevant information they need. furthermore, the publications include domain-specific named entities and relations that challenge the general-purpose search engines. therefore, it is of critical importance to build a search engine that can find relevant documents in this terminology-rich and domain-specific literature. biomedical named entity recognition (ner) and relation extraction can be utilized to semantically structure publications around the biochemically related entities. when named entities and their relations are extracted, a document can be expressed as a set of triplets of the form (entity1, entity2, relation). this formulation can be converted to an inverted index from the related entities to the publications in order to enable retrieving relevant documents to a query by entity and relation matching. if the same entities are referenced with different words (e.g. ace2, angiotensin-converting enzyme 2, q9byf1), named entity normalization can be used to identify different mentions of the same entity. enhanced with named entity normalization, the inverted index can retrieve the documents that contain biochemical relations of a free-text query, as grouped by the related biomolecules. in this work, we present vapur, an online search engine to find related protein -compound pairs in the covid-19 anthology. vapur is empowered with a biochemical relation-based inverted index that is created through named entity recognition, named entity normalization, and relation extraction on cord-19 abstracts @xcite . thanks to the underlying biochemical domainspecific tools and relation extraction model, vapur identifies biochemically related entities to a freetext query and retrieves the publications that mention the relation. the design of vapur offers a novel approach to explore covid-19 literature with a fo-figure 1 : the workflow of the pipeline behind vapur. we first split cord-19 abstracts to sentences and use bern to detect and normalize the entities in the text. we then identify the biochemical relations with the relation extraction model that we trained and reform the output as an inverted index of relations. vapur leverages this inverted index to retrieve relevant publications to the query as categorized by related entities. ## related work keeping up to date with the growing body of biomedical literature is a big challenge and almost a kind of magic in scientific research. efforts such as chemprot @xcite aim to promote research in this direction by presenting pubmed abstracts in which proteins, chemicals, and their relations are manually labeled. @xcite @xcite developed sequential models for chemical and protein relation extraction on chemprot, while @xcite used an ensemble of deep models with svm. however, all these models depend on the named entities to be recognized beforehand. named entity recognition and normalization are widely studied topics in biomedical text mining to extract and link entities. conditional random fields (crf) was a popular approach in early biomedical ner studies @xcite @xcite and with the rise of deep learning, sequential models were also integrated @xcite . recently, transformers-based ner models attracted more attention, including bern @xcite , which is a state-ofthe-art biomedical named entity recognition and normalization tool that uses biobert @xcite to identify and normalize the entities in a sentence. bern is adopted by recent generaldomain biomedical text mining studies @xcite as well as a model to answer covid-19 related questions based on cord-19 @xcite . cord-19 @xcite is a data set comprising scientific publications related to covid-19 and it has provided a valuable resource for text mining studies @xcite @xcite , including covid-19 specific search engine development such as semviz @xcite and kd-covid foot_0 . semviz aims to present subtle relations between entities by semantic visualization of a cord-19 based knowledge-graph. kd-covid, on the other hand, identifies the most similar sentence in cord-19 to a query and retrieves the corresponding publication. kd-covid also provides links to large protein, disease, and protein-disease relation databases. vapur is different from these approaches, since it is based on an inverted index from biochemically related entities to publications. while most other systems depend on existing knowledge bases to provide information about related entities, vapur uses nlp techniques to automatically extract the most up-to-date information from the scientific literature. ## results and discussion in order to assess the performance of vapur, we evaluated the performance of its components both as separate modules and together as an end-to-end pipeline. since bern, the named entity recognition and normalization module, has already been shown to be successful @xcite , we focus on automatic evaluation of the relation extraction model and expert evaluation of vapur in this work. ## conclusion we present vapur, a search engine with an emphasis on identifying protein -chemical relations in the covid-19 domain. to the best of our knowledge, vapur is the first search engine that uses relation extraction to construct an inverted index of related biochemical entities beyond knowledge bases. thanks to the relation extraction model, vapur is able to categorize documents relevant to a query by biochemical entities related to it. we evaluated the relation extraction model on chemprot and observed that biobert-based model has the highest performance. expert evaluation of the sample annotated sentences from cord-19 showed that vapur successfully finds relations between entities when the entities are identified and normalized accurately. finally, six domain experts used vapur with 5 different queries and expressed that vapur is easy to use, retrieves results that are related to the queries, and it is useful for future research. although we focused on covid-19 in this work, our approach can find application in any biomedical domain with a different choice of database to index. we believe that domain-specific scientific search engines will gain more interest in the future since covid-19 is unlikely to be the last global health crisis. consequently, we plan to improve vapur by integrating full-papers, developing more robust named entity recognizers and normalizers, and indexing text databases on different domains. in order to help similar efforts, we make the underlying pipeline and vapur available at https://github.com/ boun-tabi/vapur and https://tabilab.cmpe. boun.edu.tr/vapur/ , respectively.