Generating explanations for complex biomedical queries

Recent advances in health and life sciences have led to generation of a large amount of biomedical data. To facilitate access to its desired parts, such a big mass of data has been represented in structured forms, like databases and ontologies. On the other hand, representing these databases and ontologies in different formats, constructing them independently from each other, and storing them at different locations have brought about many challenges for answering queries about the knowledge represented in these ontologies and databases. One of the challenges for the users is to be able to represent such a biomedical query in a natural language, and get its answers in an understandable form. Another challenge is to extract relevant knowledge from different knowledge resources, and integrate them appropriately using also definitions, such as, chains of gene-gene interactions, cliques of genes based on gene-gene relations, or similarity/diversity of genes/drugs. Furthermore, once an answer is found for a complex query, the experts may need further explanations about the answer. The first two challenges have been addressed earlier using Answer Set Programming (ASP), with the development of a software system (called BIOQUERY-ASP). This thesis addresses the third challenge: explanation generation in ASP. In this thesis, we extend the earlier work on the first two challenges, to new forms of biomedical queries (e.g., about drug similarity) and to new biomedical knowledge resources. We introduce novel mathematical models and algorithms to generate (shortest or k different) explanations for queries in ASP, and provide a comprehensive theoretical analysis of these methods. We implement these algorithms and integrate them in BIOQUERY-ASP, and provide an experimental evaluation of our methods with some complex biomedical queries over the biomedical knowledge resources PHARMGKB, DRUGBANK, BIOGRID, CTD, SIDER, DISEASEONTOLOGY and ORPHADATA

Finding answers and generating explanations for complex biomedical queries

We present new methods to efficiently answer complex queries over biomedical ontologies and databases considering the relevant parts of these knowledge resources, and to generate shortest explanations to justify these answers. Both algorithms rely on the high-level representation and efficient solvers of Answer Set Programming. We apply these algorithms to find answers and explanations to some complex queries related to drug discovery, over PHARMGKB, DRUGBANK, BIOGRID, CTD and SIDER

A general formal framework for pathfinding problems with multiple agents

Pathfinding for a single agent is the problem of planning a route from an initial location to a goal location in an environment, going around obstacles. Pathfinding for multiple agents also aims to plan such routes for each agent, subject to different constraints, such as restrictions on the length of each path or on the total length of paths, no self-intersecting paths, no intersection of paths/plans, no crossing/meeting each other. It also has variations for finding optimal solutions, e.g., with respect to the maximum path length, or the sum of plan lengths. These problems are important for many real-life applications, such as motion planning, vehicle routing, environmental monitoring, patrolling, computer games. Motivated by such applications, we introduce a formal framework that is general enough to address all these problems: we use the expressive high-level representation formalism and efficient solvers of the declarative programming paradigm Answer Set Programming. We also introduce heuristics to improve the computational efficiency and/or solution quality. We show the applicability and usefulness of our framework by experiments, with randomly generated problem instances on a grid, on a real-world road network, and on a real computer game terrain