Context-aware query design combines knowledge and data for efficient reading and reasoning (2021)

Abstract: The amount of biomedical literature has vastly increased over the past few decades. As a result, the sheer quantity of accessible information is overwhelming, and complicates manual information retrieval. Automated methods seek to speed up information retrieval from biomedical literature. However, such automated methods are still too time-intensive to survey all existing biomedical literature. We present a methodology for automatically generating literature queries that select relevant papers based on biological data. By using differentially expressed genes to inform our literature searches, we focus information extraction on mechanistic signaling details that are crucial for the disease or context of interest.

## introduction the number of peer-reviewed publications in molecular biology, biotechnology, and biomedical research increases exponentially every year. there is a considerable number of published papers on any one mainstream biomedical research topic, potentially hundreds of thousands of relevant articles. for many areas of study, simply reading every paper is unrealistic, or even physically impossible. when studying biological systems, such as intracellular signaling networks, this problem is apparent -accurate representation of all relevant signaling events requires extensive, expert knowledge acquired over many years of study. by using natural language processing, machine readers are capable of extracting interactions from hundreds or thousands of papers in a matter of hours, achieving a substantial speedup over manual information extraction @xcite . for this reason, automated methods for information extraction, such as machine reading, are used to retrieve information about intracellular signaling networks, and this information can then be used for model assembly or extension. while automated methods accelerate model assembly, the time required for processing all selected papers still depends on the number and the type of papers chosen for machine reading @xcite . to retrieve relevant papers for machine reading, a common method is to query databases that contain biomedical literature. one repository for biomedical literature, medline, contains over 27 million papers @xcite , and a common method for retrieving papers from medline is through its associated search engine, pubmed. querying medline through pubmed is particularly useful for identifying papers on a specific context such as disease or cell type. it is also used for identification of individual proteins, signaling pathways, and general cell processes in one specific context. one example of a pubmed query that targets a single pathway in a specific context is '"hippo pathway" and "stem cells"'. this query returns 272 papers, many of which describe hippo pathway signaling trends in cancerous stem cells, as well as non-cancerous stem cells. these papers contain a wealth of information about the mechanistic causes of stemness. however, retrieval of these papers requires a priori knowledge that the hippo pathway is important in stem cell maintenance and renewal. additionally, these papers describe one small facet of stem cell signaling, and do not contain all the information needed to understand the system. to widen our perspective, we could retrieve all papers in medline that concern stem cells by querying pubmed with "stem cells". here, we encounter two obstacles -this query returns over 271,000 papers, many of which describe morphological or anatomical details, and not signaling pathways. our dilemma is that retrieval of relevant, crucial papers requires prior knowledge of which pathways are important. there is a demand for improvement in methods for patient-specific paper retrieval, as evidenced by the trec precision medicine track @xcite . state-of-the-art methods for paper retrieval rely on term lists generated by experts or users, or automated information retrieval of similar papers (sesagiri @xcite . these methods have several disadvantages. first, paper retrieval may depend on the cooperation of one or more experts in the field. even for automated techniques that locate papers through related citations, or sematic analysis, some level of prior knowledge is needed. also, even for experts that are up to date on canonical signaling pathways in a context of interest, novel pathways or signaling events cannot be easily targeted in a literature query. for efficient, thorough, contextaware exploration of cellular signaling, improved methods for literature retrieval are needed. we present here a methodology for automated query design that does not rely on manual steps of the domain expert. to address the potential role of differentially expressed genes (degs) in disease mechanisms, we infer queries from biological data. under-or over-expressed genes in the disease state are often the ones that play a role in disease progression @xcite . identifying these contextual degs and using them as query terms focuses literature reading on genes and proteins that have altered signaling trends, and therefore, it facilitates further exploration of intracellular signaling networks that are potentially affected in disease. our method utilizes gene expression data to find possible genes of interest based on their relative expression changes in response to disease, infection, etc. these genes of interest are used in the query to narrow down all possible pubmed hits to relevant signaling papers only. furthermore, we also take into consideration how well-known each gene is, to choose the optimal number of gene terms in a query. our results show that automated query design using these methods returns relevant signaling papers, and interactions extracted from these papers are informative and useful when reasoning about the queried context. this addresses a well-established problem in precision medicine -altered signaling pathways are often unique to one patient or environment and are difficult to study manually. our methodology can be used in conjunction with any state-of-the-art model assembly techniques to aid in understanding affected signaling mechanisms in patient or cell line-specific systems. this methodology will provide an automated framework to retrieve research papers and streamline the process of model assembly. our proposed automated query design methodology is outlined in figure 1 . ## query design method in the following sub-sections, we describe our method to identify degs in the context of a disease, cell line, tissue type, or other condition (e.g., drug treatments), and for using them to form query terms when searching literature. ## results to demonstrate the usefulness of our automated query design methodology, we show results for four different contexts. for each context, we automatically design two queries, one with an expected large number of output papers, and one with an expected small number of output papers. these results illustrate how degs can be used to formulate queries that output relevant papers, and how the annotation score affects the volume of papers. we also show that the papers contain interactions that are closely related and are involved in the same go biological processes. ## conclusions while automated methods for extracting interactions from literature have improved the speed of information extraction, this process still has its pitfalls. specifically, finding all relevant literature for the context at hand can be difficult, and brute force methods for selecting papers are too slow. by incorporating biological data in our queries, we can select relevant literature, and control the size of the reading sets. our results show that using degs to formulate queries allows for targeting literature that could help explain differentially regulated pathways in disease. one side effect of this method is identification of degs in disease where there is little to no literature presence. in such cases, our proposed method could become critical, as it automatically identifies the gaps in our collective knowledge of certain diseases, and thus, suggests important research directions. for degs that return no additional results when used as a query term, this indicates the gene has an undiscovered role in the context of interest. future directions include refining the query formulation methodology, as well as expanding our results. the relative presence of different diseases in pubmed affects the size of the reading set, independent of the number of gene query terms. by incorporating preliminary data on the presence of a disease or context in pubmed, we can adjust the annotation score. additionally, since this method hinges on a list of affected genes or proteins with quantifiable differences from a control state, other measures of relative changes in cell function could also be used. data on changes in post-translational modification of proteins, changes in epigenetic markers such as methylation, open chromatin, or histone modifications, or even somatic mutations could also be used, especially as such entities and events can be output by the state-of-the-art machine reading. testing our methods on different datasets would help showcase the usefulness of our approach. in the future, we would also like to compare our method to a literature corpus assembled by an expert. however, since our queries are based on cell line-specific gene expression datasets, there are no existing corpuses for comparison. future work includes assembling said corpuses and comparing to our method presented in this work.