An automatic hypothesis generation for plausible linkage between xanthium and diabetes.

There has been a significant increase in text mining implementation for biomedical literature in recent years. Previous studies introduced the implementation of text mining and literature-based discovery to generate hypotheses of potential candidates for drug development. By conducting a hypothesis-generation step and using evidence from published journal articles or proceedings, previous studies have managed to reduce experimental time and costs. First, we applied the closed discovery approach from Swanson's ABC model to collect publications related to 36 Xanthium compounds or diabetes. Second, we extracted biomedical entities and relations using a knowledge extraction engine, the Public Knowledge Discovery Engine for Java or PKDE4J. Third, we built a knowledge graph using the obtained bio entities and relations and then generated paths with Xanthium compounds as source nodes and diabetes as the target node. Lastly, we employed graph embeddings to rank each path and evaluated the results based on domain experts' opinions and literature. Among 36 Xanthium compounds, 35 had direct paths to five diabetes-related nodes. We ranked 2,740,314 paths in total between 35 Xanthium compounds and three diabetes-related phrases: type 1 diabetes, type 2 diabetes, and diabetes mellitus. Based on the top five percentile paths, we concluded that adenosine, choline, beta-sitosterol, rhamnose, and scopoletin were potential candidates for diabetes drug development using natural products. Our framework for hypothesis generation employs a closed discovery from Swanson's ABC model that has proven very helpful in discovering biological linkages between bio entities. The PKDE4J tools we used to capture bio entities from our document collection could label entities into five categories: genes, compounds, phenotypes, biological processes, and molecular functions. Using the BioPREP model, we managed to interpret the semantic relatedness between two nodes and provided paths containing valuable hypotheses. Lastly, using a graph-embedding algorithm in our path-ranking analysis, we exploited the semantic relatedness while preserving the graph structure properties.

Toward a Coronavirus Knowledge Graph.

This study builds a coronavirus knowledge graph (KG) by merging two information sources. The first source is Analytical Graph (AG), which integrates more than 20 different public datasets related to drug discovery. The second source is CORD-19, a collection of published scientific articles related to COVID-19. We combined both chemo genomic entities in AG with entities extracted from CORD-19 to expand knowledge in the COVID-19 domain. Before populating KG with those entities, we perform entity disambiguation on CORD-19 collections using Wikidata. Our newly built KG contains at least 21,700 genes, 2500 diseases, 94,000 phenotypes, and other biological entities (e.g., compound, species, and cell lines). We define 27 relationship types and use them to label each edge in our KG. This research presents two cases to evaluate the KG's usability: analyzing a subgraph (ego-centered network) from the angiotensin-converting enzyme (ACE) and revealing paths between biological entities (hydroxychloroquine and IL-6 receptor; chloroquine and STAT1). The ego-centered network captured information related to COVID-19. We also found significant COVID-19-related information in top-ranked paths with a depth of three based on our path evaluation.