Context-aware information selection and model recommendation with ACCORDION

Abstract

Querying new information from knowledge sources, in general, and published literature, in particular, aims to provide precise and quick answers to questions raised about a system under study. In this paper, we present ACCORDION (ACCelerating and Optimizing model RecommenDatIONs), a novel methodology and a tool to enable efficient addressing of biological questions, by automatically recommending models that recapitulate desired dynamic behavior. Our approach integrates information extraction from literature, clustering, simulation and formal analysis to allow for automated, consistent, and robust assembling, testing and selection of context-specific models. We used ACCORDION in nine benchmark case studies and compared its performance with other previously published tools. ACCORDION is comprehensive as it can capture all relevant knowledge from literature, obtained by automated literature search and machine reading. At the same time, as our results show, ACCORDION is selective, recommending only the most relevant and useful subset (15-20%) of candidate model extensions found in literature, while guided by baseline model context and goal properties. ACCORDION is very effective, also demonstrated by our results, as it can reduce the error of the initial baseline model by more than 80%, recommending models that closely recapitulate desired behavior, and outperforming previously published tools. In this process, ACCORDION can also suggest more than one highly scored model, thus providing alternative solutions to user questions and novel insights for treatment directions.