Towards using Probabilities and Logic to Model Regulatory Networks

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2014

Autores

Antonio Goncalves (Autor) Outra; Irene Ong (Autor) Outra; Jeffrey A Lewis (Autor) Outra; Vitor Santos Costa (Autor) FCUP

Ata de Conferência Internacional

Título: 2014 IEEE 27TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS)

Páginas: 239-242

27th IEEE International Symposium on Computer-Based Medical Systems (CBMS)

New York, NY, MAY 27-29, 2014

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 1 Citação

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Biotecnologia ambiental

Outras Informações

ID Authenticus: P-009-TZS

DOI: 10.1109/cbms.2014.9

Abstract (EN): Transcriptional regulation plays an important role in every cellular decision. Unfortunately, understanding the dynamics that govern how a cell will respond to diverse environmental cues is difficult using intuition alone. We introduce logic-based regulation models based on state-of-the-art work on statistical relational learning, and validate our approach by using it to analyze time-series gene expression data of the Hog1 pathway. Our results show that plausible regulatory networks can be learned from time series gene expression data using a probabilistic logical model. Hence, network hypotheses can be generated from existing gene expression data for use by experimental biologists.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 4