Discovering Differentially Expressed Genes in Yeast Stress Data
- Resource Type
- Conference
- Authors
- Goncalves, Antonio; Ong, Irene; Lewis, Jeffrey A.; Costa, Vitor Santos
- Source
- 2014 IEEE 27th International Symposium on Computer-Based Medical Systems Computer-Based Medical Systems (CBMS), 2014 IEEE 27th International Symposium on. :537-538 May, 2014
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Stress
Encoding
Gene expression
Correlation
Electronic mail
Heating
Bioinformatics
Gene Regulation
Genomics
Data Analysis
- Language
- ISSN
- 1063-7125
2372-9198
Transcriptional regulation plays an important role in every cellular decision. Gaining an understanding of the dynamics that govern how a cell will respond to diverse environmental cues is difficult using intuition alone. We try to discover how genes interact when submitted to stress by exploring techniques of gene expression data analysis. We use several types of data, including high-throughput data. These results will help us recreate plausible regulatory networks by using a probabilistic logical model. Hence, network hypotheses can be generated from existing gene expression data for use by experimental biologists.