Lea Eckhart
November 28, 2022 2023-11-10 11:17Lea Eckhart
Researcher
My name is Lea Eckhart. Since October 2020, I am working at the chair of Prof. Dr. Hans-Peter Lenhof. My research is focused on personalized medicine and drug sensitivity prediction in cancer using machine learning algorithms.
Contact
Center for Bioinformatics
Saarland Informatics Campus
Saarland University
Building E2.1
P.O. Box 15 11 50
66041 Saarbrücken
Germany
Research
- Cancer Research
- Personalized Medicine
- Machine Learning
Education
- M. Sc. in Computational Molecular Biology, Saarland University July 2020
- B. Sc. in Computational Molecular Biology, Saarland University November 2017
Latest Publications
Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest method
[LEG+22] Lenhof, K., Eckhart, L., Gerstner, N., Kehl, T., Lenhof, H.-P. Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest method. Scientific Reports, 2022. DOI: 10.1038/s41598-022-17609-x.
GeneTrail: A Framework for the Analysis of High-Throughput Profiles
[GKL+21] Gerstner, N., Kehl, T., Lenhof, K., Eckhart, L., Schneider, L., Stöckel, D., Backes, C., Meese, E., Keller, A., Lenhof, H.-P. GeneTrail: A Framework for the Analysis of High-Throughput Profiles. Frontiers in Molecular Biosciences, 2021. DOI: 10.3389/fmolb.2021.716544.
MERIDA: a novel Boolean logic-based integer linear program for personalized cancer therapy
[LGK+21] Lenhof, K., Gerstner, N., Kehl, T., Eckhart, L., Schneider, L., Lenhof, H.-P. MERIDA: a novel Boolean logic-based integer linear program for personalized cancer therapy. Bioinformatics, btab 546, 2021. DOI: 10.1093/bioinformatics/btab546.
GeneTrail 3: advanced high-throughput enrichment analysis
[GKL+20] Gerstner, N., Kehl, T., Lenhof, K., Müller, A., Mayer, C., Eckhart, L., Grammes, N.L., Diener, C., Hart, M., Hahn, O., Walter, J., Wyss-Coray, T., Meese, E., Keller, A., Lenhof, H.-P. GeneTrail 3: advanced high-throughput enrichment analysis. Nucleic Acids Research, 2020. DOI:10.1093/nar/gkaa306.