RESUMEN
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. We have implemented this approach in the Integrated Network and Dynamical Reasoning Assembler (INDRA), which draws on existing natural language processing systems as well as pathway information in Pathway Commons and other online resources. We demonstrate the use of INDRA and natural language to model three biological processes of increasing scope: (i) p53 dynamics in response to DNA damage, (ii) adaptive drug resistance in BRAF-V600E-mutant melanomas, and (iii) the RAS signaling pathway. The use of natural language makes the task of developing a model more efficient and it increases model transparency, thereby promoting collaboration with the broader biology community.
Asunto(s)
Regulación Neoplásica de la Expresión Génica , Melanoma/genética , Modelos Genéticos , Procesamiento de Lenguaje Natural , Redes Neurales de la Computación , Neoplasias Cutáneas/genética , Antineoplásicos/uso terapéutico , Línea Celular Tumoral , Simulación por Computador , Daño del ADN , Resistencia a Antineoplásicos/genética , Inhibidores Enzimáticos/uso terapéutico , Humanos , Indoles/uso terapéutico , Lenguaje , Melanoma/tratamiento farmacológico , Melanoma/metabolismo , Melanoma/patología , Proteínas Proto-Oncogénicas B-raf/genética , Proteínas Proto-Oncogénicas B-raf/metabolismo , Proteínas Proto-Oncogénicas p21(ras)/genética , Proteínas Proto-Oncogénicas p21(ras)/metabolismo , Transducción de Señal , Neoplasias Cutáneas/tratamiento farmacológico , Neoplasias Cutáneas/metabolismo , Neoplasias Cutáneas/patología , Sulfonamidas/uso terapéutico , Proteína p53 Supresora de Tumor/genética , Proteína p53 Supresora de Tumor/metabolismo , VemurafenibRESUMEN
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. Chester builds on our prior experience constructing conversational assistants in other domains. The emphasis of this paper is on the portability of our generic spoken dialogue technology, and presents a case study of the application of these techniques to the development of a dialogue system for health communication.