Towards using Probabilities and Logic to Model Regulatory Networks

• Type: Article in International Conference Proceedings Book
• Date: 2014

Authors

Antonio Goncalves (Author) Other; Irene Ong (Author) Other; Jeffrey A Lewis (Author) Other; Vitor Santos Costa (Author) FCUP

Conference proceedings International

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

Pages: 239-242

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

New York, NY, MAY 27-29, 2014

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 1 Citation

Scientific classification

FOS: Engineering and technology > Environmental biotechnology

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4