Predicting future discoveries from current scientific literature.
Knowledge discovery in biomedicine is a time-consuming process starting from the basic research, through preclinical testing, towards possible clinical applications. Crossing of conceptual boundaries is often needed for groundbreaking biomedical research that generates highly inventive discoveries. We demonstrate the ability of a creative literature mining method to advance valuable new discoveries based on rare ideas from existing literature. When emerging ideas from scientific literature are put together as fragments of knowledge in a systematic way, they may lead to original, sometimes surprising, research findings. If enough scientific evidence is already published for the association of such findings, they can be considered as scientific hypotheses. In this chapter, we describe a method for the computer-aided generation of such hypotheses based on the existing scientific literature. Our literature-based discovery of NF-kappaB with its possible connections to autism was recently approved by scientific community, which confirms the ability of our literature mining methodology to accelerate future discoveries based on rare ideas from existing literature.
University of Nova Gorica, Vipavska 13, 5000, Nova Gorica, Slovenia, ingrid.petric@ung.si.
Ingrid
I
Petrič
Bojan
B
Cestnik
D000818:Animals; D001321:Autistic Disorder / Q000235:genetics / Q000378:metabolism; D057225:Data Mining / Q000379:methods*; D016206:Databases, Bibliographic; D006801:Humans; D063369:Knowledge Discovery / Q000379:methods*; D016328:NF-kappa B / Q000235:genetics / Q000378:metabolism
D016328:NF-kappa B