Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/3710
Title: Towards using Probabilities and Logic to Model Regulatory Networks
Authors: António José Gonçalves
Ong,I
Lewis,JA
Vítor Santos Costa
Issue Date: 2014
Abstract: 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.
URI: http://repositorio.inesctec.pt/handle/123456789/3710
http://dx.doi.org/10.1109/cbms.2014.9
metadata.dc.type: conferenceObject
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