Path Knowledge Discovery: Multilevel Text Mining as a Methodology for Phenomics

Transdisciplinary research is a rapidly expanding part of science and engineering, demanding new methods for connecting results across fields. In biomedicine for example, modeling complex biological systems requires linking knowledge across multi-level of science, from genes to disease. The move to multilevel research requires new strategies; in this discussion we present path knowledge discovery, a novel methodology for linking published research findings. The development of path knowledge discovery was motivated by problems in neuropsychiatry, where researchers need to discover interrelationships extending across brain biology that link genotype (such as dopamine gene mutations) to phenotype (observable characteristics of organisms such as cognitive performance measures). To advance an understanding of the complex bases of neuropsychiatric diseases, researchers need to search and discover relations among the many manifestations of these diseases across multiple biological and behavioral levels (i.e., genotypes and phenotypes at levels from molecular expression through complex syndromes). Phenomics - the study of phenotypes on a genome-wide scale requires close collaboration among specialists in multiple fields. We developed a computer-aided path knowledge discovery methodology to accomplish this goal. Path knowledge discovery consists of two integral tasks: 1) association path mining among concepts in multipart phenotypes that cross disciplines, and 2) fine-granularity knowledge-based content retrieval along the path(s) to permit deeper analysis. Implementing this methodology with our PhenoMining tools has required development of innovative measures of association strength for pairwise associations, as well as the strength for sequences of associations, in addition to powerful lexicon-based association expansion to increase the scope of matching. In our discussions we describe the validation of the methodology using a published heritability study from cognition research, and we obtain comparable results. We show how PhenoMining tools can greatly reduce a domain expert's time (by several orders of magnitude) when searching and gathering knowledge from the published literature, and can facilitate derivation of interpretable results. We built these PhenoMining tools on an existing knowledge base (PhenoWiki. org), now called PhenoWiki+, which can greatly speed up the knowledge acquisition process. Further, using the Resource Description Framework (RDF) data model in the PhenoWiki knowledge repository allows us to connect with different knowledge sources to enlarge the knowledge scope. The knowledge base also supports annotation, an important capability for collaborative knowledge discovery.