Research disciplines
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Natural sciences
- Scientific computing
- Bioinformatics and computational biology
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Medical and health sciences
- Bioinformatics and computational biology
- Bioinformatics and computational biology
- Public health care
- Public health services
- Bioinformatics and computational biology
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Engineering and technology
- Scientific computing
Project description
Understanding gene expression at a systems level is crucial for gaining correct insights in development and disease of complex organisms. Transcription regulatory networks describe gene expression as a function of inputs specified by physical and funcitonal interactions between transcription factors, miRNAs, cofactors and DNA. We investigate reverse-engineering algorithms to more correctly infer transcription regulatory networks from different genome-wide datasets.
