From word models to executable models of signaling networks using automated assembly.

Word models (natural language descriptions of molecular mechanisms) are a common currency in spoken and written communication in biomedicine but are of limited use in predicting the behavior of complex biological networks. We present an approach to building computational models directly from natural language using automated assembly. Molecular mechanisms described in simple English are read by natural language processing algorithms, converted into an intermediate representation, and assembled into executable or network models.

Chester: towards a personal medication advisor.

Dialogue systems for health communication hold out the promise of providing intelligent assistance to patients through natural interfaces that require no training to use. But in order to make the development of such systems cost effective, we must be able to use generic techniques and components which are then specialized as needed to the specific health problem and patient population. In this paper, we describe Chester, a prototype intelligent assistant that interacts with its user via conversational natural spoken language to provide them with information and advice regarding their prescribed medications.