Grimm DG - Search Results

1

A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species.

John M, Haselbeck F, Dass R, Malisi C, Ricca P, Dreischer C, Schultheiss SJ, Grimm DG. John M, et al. Among authors: grimm dg. Front Plant Sci. 2022 Nov 4;13:932512. doi: 10.3389/fpls.2022.932512. eCollection 2022. Front Plant Sci. 2022. PMID: 36407627 Free PMC article.

2

Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops.

Genze N, Bharti R, Grieb M, Schultheiss SJ, Grimm DG. Genze N, et al. Among authors: grimm dg. Plant Methods. 2020 Dec 22;16(1):157. doi: 10.1186/s13007-020-00699-x. Plant Methods. 2020. PMID: 33353559 Free PMC article.

3

Efficient permutation-based genome-wide association studies for normal and skewed phenotypic distributions.

John M, Ankenbrand MJ, Artmann C, Freudenthal JA, Korte A, Grimm DG. John M, et al. Among authors: grimm dg. Bioinformatics. 2022 Sep 16;38(Suppl_2):ii5-ii12. doi: 10.1093/bioinformatics/btac455. Bioinformatics. 2022. PMID: 36124808 Free PMC article.

4

easyPheno: An easy-to-use and easy-to-extend Python framework for phenotype prediction using Bayesian optimization.

Haselbeck F, John M, Grimm DG. Haselbeck F, et al. Among authors: grimm dg. Bioinform Adv. 2023 Mar 22;3(1):vbad035. doi: 10.1093/bioadv/vbad035. eCollection 2023. Bioinform Adv. 2023. PMID: 37066135 Free PMC article.

5

Predicting Gene Regulatory Interactions Using Natural Genetic Variation.

John M, Grimm D, Korte A. John M, et al. Methods Mol Biol. 2023;2698:301-322. doi: 10.1007/978-1-0716-3354-0_18. Methods Mol Biol. 2023. PMID: 37682482

6

Superior protein thermophilicity prediction with protein language model embeddings.

Haselbeck F, John M, Zhang Y, Pirnay J, Fuenzalida-Werner JP, Costa RD, Grimm DG. Haselbeck F, et al. Among authors: grimm dg. NAR Genom Bioinform. 2023 Oct 11;5(4):lqad087. doi: 10.1093/nargab/lqad087. eCollection 2023 Dec. NAR Genom Bioinform. 2023. PMID: 37829176 Free PMC article.

7

The benefits of permutation-based genome-wide association studies.

John M, Korte A, Grimm DG. John M, et al. Among authors: grimm dg. J Exp Bot. 2024 Sep 11;75(17):5377-5389. doi: 10.1093/jxb/erae280. J Exp Bot. 2024. PMID: 38954539 Free PMC article.

8

Guiding questions to avoid data leakage in biological machine learning applications.

Bernett J, Blumenthal DB, Grimm DG, Haselbeck F, Joeres R, Kalinina OV, List M. Bernett J, et al. Among authors: grimm dg. Nat Methods. 2024 Aug;21(8):1444-1453. doi: 10.1038/s41592-024-02362-y. Epub 2024 Aug 9. Nat Methods. 2024. PMID: 39122953 Review.

9

Population-aware permutation-based significance thresholds for genome-wide association studies.

John M, Korte A, Todesco M, Grimm DG. John M, et al. Among authors: grimm dg. Bioinform Adv. 2024 Oct 28;4(1):vbae168. doi: 10.1093/bioadv/vbae168. eCollection 2024. Bioinform Adv. 2024. PMID: 39678204 Free PMC article.

10

Combining machine learning and optimization for the operational patient-bed assignment problem.

Schäfer F, Walther M, Grimm DG, Hübner A. Schäfer F, et al. Among authors: grimm dg. Health Care Manag Sci. 2023 Dec;26(4):785-806. doi: 10.1007/s10729-023-09652-5. Epub 2023 Nov 28. Health Care Manag Sci. 2023. PMID: 38015289 Free PMC article.

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