Machine learning
My research involves the development and application of computational methods with the ultimate goal to understand plant development. In particular, I am interested in the question how to analyze the large amount of genome sequences and other -omics data that are available nowadays. A lot of data is gathered nowadays, but it is far from trivial to obtain biological insights from these data. To do so, I use machine learning; starting from available knowledge, these methods deliver novel predictions which are subsequently tested by colleagues working in the lab.
Typical examples of projects include:
- Prediction of functional sites in protein sequences
- Analysis of transcription factor binding sites
- Modelling of regulatory networks
- Prediction of functions of genes
- Analysis of alternative splicing
Publications
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Similarities between plant traits based on their connection to underlying gene functions
PLoS ONE 12 (2017)8. - ISSN 1932-6203 -
Sequence-based analysis of protein degradation rates
Proteins : Structure, Function, and Bioinformatics 85 (2017)9. - ISSN 0887-3585 - p. 1593 - 1601. -
An interactomics overview of the human and bovine milk proteome over lactation
Proteome Science 15 (2017). - ISSN 1477-5956 -
Species-specific genome sequence databases : A practical review
In: Plant genomics databases / , van Dijk, Aalt D.J.. - : Humana Press Inc. (Methods in Molecular Biology ) - ISBN 9781493966561 - p. 173 - 181. -
Cross-Family Transcription Factor Interactions : An Additional Layer of Gene Regulation
Trends in Plant Science 22 (2017)1. - ISSN 1360-1385 - p. 66 - 80. -
Selected proceedings of Machine Learning in Systems Biology: MLSB 2016
BMC Bioinformatics 17 (2016)suppl. 16. - ISSN 1471-2105 -
An expanded evaluation of protein function prediction methods shows an improvement in accuracy
Genome Biology 17 (2016)1. - ISSN 1474-7596 -
Mutational robustness of gene regulatory networks: interpay between transcription factor - target gene and protein-protein interactions