Finding Answers and Generating Explanations for Complex Biomedical Queries

Abstract

We present new methods to efficiently answer complex queries overbiomedical ontologies and databases considering the relevant partsof these knowledge resources, and to generate shortest explanationsto justify these answers. Both algorithms rely on the high-levelrepresentation and efficient solvers of Answer Set Programming. Weapply these algorithms to find answers and explanations to some complexqueries related to drug discovery, over PharmGKB, DrugBank, BioGrid, CTD and Sider.

Applications of Answer Set Programming

“…With a declarative representation of the problem in ASP, one can perform various reasoning tasks, such as ontological query answering and explanation generation (Le et al 2012; Erdem et al 2011; Erdem and Oztok 2015), planning and diagnosis (Tran and Baral 2009), consistency checking and explanation generation (Gebser et al 2011), and repair and prediction (Gebser et al 2010).…”

“…To answer complex queries over a variety of biomedical ontologies (Erdem et al 2011), ASP allows us to extract relevant parts of them (thanks to external atoms) and integrate them by rules. For instance, the drug names can be extracted from a drug ontology, by first extracting the relevant triples from the ontology: …”

“…To answer queries like “What are the genes related to the gene ADRB1 through a gene‐gene relation chain of length at most three?” the auxiliary concept of reachability of a gene from another gene by means of a chain of gene‐gene interactions is required (Erdem et al 2011); this concept can be defined in ASP recursively as follows: …”

“…Negation as failure is useful to represent defaults (as seen in the examples of robotics applications) and the concept of unknown. For instance, we can define that some drugs' toxicity is unknown as follows (Erdem et al 2011): …”

“…These queries are used, for instance, to find the trees that satisfy a given property (for example, whose size is smaller than a specified constant, with a specified ratio of internal nodes to external nodes), to find the similarity of two trees with respect to a distance measure (for example, the Robinson-Foulds distance), to compute clades with specific properties (for example, the minimum spanning clade for taxa in a specified tree). Erdem et al (2011) and Erdem and Oztok (2015) use ASP to answer complex queries over biomedical ontologies and databases that consider the relevant parts of these knowledge resources, and to generate the shortest explanations to justify these answers. They apply their methods to find answers and explanations to some complex queries related to drug discovery (for example, "What are the genes that are targeted by the drug Epinephrine and that interact with the gene DLG4?…”