Understanding plants through Artificial Intelligence
Planter’s Punch
Under the heading Planter’s Punch we present each month one special aspect of the CEPLAS research programme. All contributions are prepared by our early career researchers.
About the author
Simon Zumkeller is a postdoc in the group of Jędrzej Jakub Szymański at Forschungszentrum Jülich. He is an expert in plant science, bridging experience in molecular biology experimentation with machine learning to uncover how plants regulate genes systematically. By training explainable AI, on the basis of multi-omics he creates interpretable methods to understand adaption and evolution. Simon is an experienced speaker, author, and collaborator within CEPLAS and the Systems Biology and Modeling group.
Further Reading
Peleke, Fritz Forbang, Simon Maria Zumkeller, Mehmet Gültas, Armin Schmitt, and Jędrzej Jakub Szymański (2023). Deep Learning the Cis-Regulatory Code for Gene Expression in Selected Model Plants. Researchsquare. doi: 10.21203/rs.3.rs-2873437/v1
Holst, F., A. Bolger, C. Günther, J. Maß, F. Kindel, and S. Triesch (2023). Helixer—de Novo Prediction of Primary Eukaryotic Gene Models Combining Deep Learning and a Hidden Markov Model. bioRxiv 2023.02.06.527280; doi:10.1101/2023.02.06.527280
Avsec, Žiga, Vikram Agarwal, Daniel Visentin, Joseph R. Ledsam, Agnieszka Grabska-Barwinska, Kyle R. Taylor, Yannis Assael, John Jumper, Pushmeet Kohli, and David R. Kelley. (2021) Effective Gene Expression Prediction from Sequence by Integrating Long-Range Interactions. Nature Methods 18 (10): 1196-1203. doi: 10.1038/s41592-021-01252-x