| A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species M John, F Haselbeck, R Dass, C Malisi, P Ricca, C Dreischer, ... Frontiers in Plant Science 13, 932512, 2022 | 12 | 2022 |
| Efficient permutation-based genome-wide association studies for normal and skewed phenotypic distributions M John, MJ Ankenbrand, C Artmann, JA Freudenthal, A Korte, DG Grimm Bioinformatics 38 (Supplement_2), ii5-ii12, 2022 | 12 | 2022 |
| Superior protein thermophilicity prediction with protein language model embeddings F Haselbeck, M John, Y Zhang, J Pirnay, JP Fuenzalida-Werner, ... NAR Genomics and Bioinformatics 5 (4), lqad087, 2023 | 4 | 2023 |
| Predicting Gene Regulatory Interactions Using Natural Genetic Variation M John, D Grimm, A Korte Plant Gene Regulatory Networks: Methods and Protocols, 301-322, 2023 | | 2023 |
| permGWAS2: Enhanced and Accelerated Permutation-based Genome-Wide Association Studies M John, A Korte, DG Grimm bioRxiv, 2023.11. 28.569016, 2023 | | 2023 |
| easyPheno: An easy-to-use and easy-to-extend Python framework for phenotype prediction using Bayesian optimization F Haselbeck, M John, DG Grimm Bioinformatics Advances 3 (1), vbad035, 2023 | | 2023 |
