An up-to-date list of publications of Prof. Dr. Dominik Grimm can be found on Google Scholar or on ResearchGate.
PEER REVIEWED ARTICLES
2024
- Deep reinforcement learning enables conceptual design of processes for separating azeotropic mixtures without prior knowledge
Q Göttl*, J Pirnay*, J Burger**, DG Grimm**
Computers and Chemical Engineering, 2024 (https://doi.org/10.1016/j.compchemeng.2024.108975) [Code]
- Population-aware permutation-based significance thresholds for genome-wide association studies
M John, A Korte, M Todesco, DG Grimm
Bioinformatics Advances, 2024 ( https://doi.org/10.1093/bioadv/vbae168 ) [Code]
- Guiding questions to avoid data leakage in biological machine learning applications
J Bernett, DB Blumenthal*, DG Grimm*, F Haselbeck, R Joeres, OV Kalinina*, M List*
Nature Methods, 2024 ( https://doi.org/10.1038/s41592-024-02362-y ) Alphabetical author order, equal contributions, * joint corresponding authors
- Take a Step and Reconsider: Sequence Decoding for Self-Improved Neural Combinatorial Optimization
J Pirnay, DG Grimm
European Conference on Artificial Intelligence (ECAI), 2024 (https://doi.org/10.3233/FAIA240707) [Code, arXiv]
- The Benefits of Permutation-Based Genome-Wide Association Studies
M John, A Korte, DG Grimm
Journal of Experimental Botany, 2024 (https://doi.org/10.1093/jxb/erae280)
- Self-Improvement for Neural Combinatorial Optimization: Sample Without Replacement, but Improvement
J Pirnay, DG Grimm
Transactions on Machine Learning Research (TMLR), 2024 (https://openreview.net/forum?id=agT8ojoH0X) [Code]Awarded with “Featured” Certification
- Forecasting Seasonally Fluctuating Sales of Perishable Products in the Horticultural Industry
J Eiglsperger, F Haselbeck, V Stiele, C Guadarrama Serrano, K Lim-Trinh, K Menrad, T Hannus, DG Grimm
Expert Systems with Applications, 2024 (https://doi.org/10.1016/j.eswa.2024.123438)
- Manually annotated and curated Dataset of diverse Weed Species in Maize and Sorghum for Computer Vision
N Genze, WK Vahl, J Groth, M Wirth, M Grieb, DG Grimm
Scientific Data, 2024 (https://www.nature.com/articles/s41597-024-02945-6) [Data]
2023
- Superior Protein Thermophilicity Prediction With Protein Language Model Embeddings
F Haselbeck, M John, Y Zhang, J Pirnay, JP Fuenzalida-Werner, RD Costa, DG Grimm
NAR Genomics and Bioinformatics, 2023 (https://doi.org/10.1093/nargab/lqad087) [Code]
- Combining Machine Learning and Optimization for the Operational Patient-Bed Assignment Problem
F Schäfer, M Walther, DG Grimm, A Hübner
Health Care Management Science, 2023 (https://doi.org/10.1007/s10729-023-09652-5)
- Improved Weed Segmentation in UAV Imagery of Sorghum Fields with a Combined Deblurring Segmentation Model
N Genze, M Wirth, C Schreiner, Raymond Ajewkwe, M Grieb, DG Grimm
Plant Methods, 2023 (https://doi.org/10.1186/s13007-023-01060-8) [Code, Data]
- Convex Envelope Method for determining liquid multi-phase equilibria in systems with arbitrary number of components
Q Göttl, J Pirnay, DG Grimm, J Burger
Computers and Chemical Engineering, 2023 (https://doi.org/10.1016/j.compchemeng.2023.108321) [Code, Preprint]
- ForeTiS: A Comprehensive Time Series Forecasting Framework in Python
J Eiglsperger, F Haselbeck, DG Grimm
Machine Learning with Applications, 2023 ( https://doi.org/10.1016/j.mlwa.2023.100467) [Code, Documentation]
- 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, 2023 (https://doi.org/10.1093/bioadv/vbad035) [Code, Documentation]
- Policy-Based Self-Competition for Planning Problems
J Pirnay,Q Göttl, J Burger, DG Grimm
International Conference on Learning Representations (ICLR), 2023 (https://openreview.net/forum?id=SmufNDN90G) [Code, Video]
2022
- HeliantHOME, a public and centralized database of phenotypic sunflower data
N Bercovich, N Genze, M Todesco, H Owens, JS Legare, K Huang, L Rieseberg, DG Grimm
Scientific Data, 2022 (https://www.nature.com/articles/s41597-022-01842-0) [HeliantHome Website, Code]
- Systematic analysis of the underlying genomic architecture for transcriptional-translational coupling in prokaryotes
R Bharti, D Siebert, B Blombach*, DG Grimm*
NAR Genomics and Bioinformatics, 2022 (https://doi.org/10.1093/nargab/lqac074) [Code]
- Rat Hepatic Stellate Cell Line CFSC-2G: Genetic Markers and Short Tandem Repeat Profile Useful for Cell Line Authentication
I Nanda, SK Schröder, C Steinlein, T Haaf, EM Buhl, DG Grimm, R Weiskirchen
Cells, 2022 (https://doi.org/10.3390/cells11182900)
- Deep Learning-based Early Weed Segmentation using Motion Blurred UAV Images of Sorghum Fields
N Genze, R Ajekwe, Z Güreli, F Haselbeck, M Grieb, DG Grimm
Computers and Electronics in Agriculture, 2022 (https://doi.org/10.1016/j.compag.2022.107388) [Code, Data]
- 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, SJ Schultheiss, DG Grimm
Frontiers in Plant Science, 2022 (https://dx.doi.org/10.3389/fpls.2022.932512) [Code]
- Dynamically Self-Adjusting Gaussian Processes for Data Stream Modelling
JD Hüwel*, F Haselbeck*, DG Grimm, Christian Beecks
KI 2022: Advances in Artificial Intelligence, 2022 (https://doi.org/10.1007/978-3-031-15791-2_10) [Code]
- Efficient Permutation-based Genome-wide Association Studies for Normal and Skewed Phenotypic Distributions
M John, M Ankenbrand, C Artmann, J Freudenthal, A Korte*, DG Grimm*
Bioinformatics (European Conference on Computational Biology (ECCB) 2022), 2022 (https://doi.org/10.1093/bioinformatics/btac455) [Code]
- Using Reinforcement Learning in a Game-like Setup for Automated Process Synthesis without Prior Process Knowledge
Q Göttl, DG Grimm, J Burger
Proceedings of the 14th International Symposium on Process Systems Engineering, 2022 (https://doi.org/10.1016/B978-0-323-85159-6.50259-1) [Code]
- Computational identification of protein complexes from network interactions: Present state, challenges, and the way forward
S Omranian, Z Nikoloski, DG Grimm
Computational and Structural Biotechnology Journal, 2022 (https://doi.org/10.1016/j.csbj.2022.05.049)
- Genetic Characterization of Rat Hepatic Stellate Cell Line HSC-T6 for In Vitro Cell Line Authentication
I Nanda, C Steinlein, T Haaf, EM Buhl, DG Grimm, SL Friedman, SK Murer, SK Schröder, R Weiskirchen
Cells, 2022 (https://doi.org/10.3390/cells11111783)
- Machine Learning Outperforms Classical Forecasting on Horticultural Sales Predictions
F Haselbeck, J Killinger, K Menrad, T Hannus, DG Grimm
Machine Learning with Applications, 2022 (https://doi.org/10.1016/j.mlwa.2021.100239) [Code]
* Equal contributions
2021
- The AIMe registry for artificial intelligence in biomedical research
J Matschinske, N Alcaraz, A Benis, M Golebiewski, DG Grimm, L Heumos, T Kacprowski, O Lazareva, M List, Z Louadi, JK Pauling, N Pfeifer, R Röttger, V Schwämmle, G Sturm, A Traverso, K Van Steen, M Vaz de Freitas, G Villalba Silva, L Wee, N Wenke, M Zanin, O Zolotareva, J Baumbach, DB Blumenthal
Nature Methods, 2021 (https://doi.org/10.1038/s41592-021-01241-0) [AIMe Web Service: https://aime-registry.org; Code: Backend, Frontend]
- Automated Flowsheet Synthesis Using Hierarchical Reinforcement Learning: proof of concept
Q Göttl, YH Tönges, DG Grimm, J Burger
Chemie Ingenieur Technik, 2021 (https://doi.org/10.1002/cite.202100086) [Code]
- EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data
F Haselbeck, DG Grimm
KI 2021: Advances in Artificial Intelligence, 2021 (https://doi.org/10.1007/978-3-030-87626-5_11, Preprint on arXiv:2107.02463) [Code]
- Automated Synthesis of Steady-State Continuous Processes using Reinforcement Learning
Q Göttl, DG Grimm, J Burger
Frontiers of Chemical Science and Engineering, 2021 (https://doi.org/10.1007/s11705-021-2055-9) [Code]
- Automated Process Synthesis Using Reinforcement Learning
Q Göttl, DG Grimm, J Burger
In Computer Aided Chemical Engineering (Vol. 50, pp. 209-214) 2021 (http://dx.doi.org/10.1016/B978-0-323-88506-5.50034-6) [Code]
2020
- Accurate Machine Learning-Based Germination Detection, Prediction and Quality Assessment of Three Grain Crops
N Genze, R Bharti, M Grieb, SJ Schultheiss, DG Grimm
Plant Methods 16, 157, 2020 (https://doi.org/10.1186/s13007-020-00699-x) [Code@GitHub, Data@Mendeley Data]
- Network-guided search for genetic heterogeneity between gene pairs
AC Gumpinger, B Rieck, DG Grimm, International Headache Genetics Consortium, K Borgwardt
Bioinformatics, (https://doi.org/10.1093/bioinformatics/btaa581) [Code]
- AraPheno and the AraGWAS Catalog 2020: A major database update including RNA-Seq and knockout mutation data for Arabidopsis thaliana
M Togninalli*, Ü Seren*, JA Freudenthal, JG Monroe, D Meng, M Nordborg, D Weigel, KM Borgwardt, A Korte, DG Grimm
Nucleic Acids Research (NAR), 48 (D1), D1063-D1068 (https://doi.org/10.1093/nar/gkz925) [Code] * Shared first authorship
2019
- Current Challenges and Best Practice Protocols for Microbiome Analysis
R Bharti, DG Grimm
Briefings in Bioinformatics (BIB), 2019 (https://doi.org/10.1093/bib/bbz155) [Code]
- Large expert curated database for benchmarking document similarity detection in biomedical literature search
P Brown, RELISH Consortium*, Y Zhou
Database, 2019 (https://academic.oup.com/database/article/doi/10.1093/database/baz085/5608006)
* As part of the RELISH Consortium
- Exosome-based detection of activating and resistance EGFR mutations from plasma of non-small cell lung cancer patients
E Castellanos-Rizaldos, X Zhang, VR Tadigotla, DG Grimm, C Karlovich, LE Raez, JK Skog
Oncotarget, 2019, 10(30): 2911-2920 (PubMed: Link)
2018
- Linking Genomic and Metabolomic Natural Variation Uncovers Nematode Pheromone Biosynthesis
JM Falcke, N Bose, AB Artyukhin, C Rödelsperger, GV Markov JJ Yim, DG Grimm, MH Claassen, O Panda, JA Baccile, YK Zhang, HH Le, D Jolic, FC Schroeder, RJ Sommer
Cell Chemical Biology, 25.6 (2018): 787-796. (Link)
- Exosome-based Detection of EGFR T790M in Plasma from Non-Small Cell Lung Cancer Patients
E Castellanos-Rizaldos, DG Grimm, V Tadigotla, J Hurley, J Healy, PL Neal, M Sher, R Venkatesan, C Karlovich, M Raponi, AK Krug, M Noerholm, J Tannous, BA Tannous, LE Real, J Skog
Clinical Cancer Research (CCR), 24.12 (2018): 2944-2950 (Link)
- The AraGWAS Catalog: A curated and standardized Arabidopsis thaliana GWAS catalog
M Togninalli*, Ü Seren*, M Dazhe, F Joffrey, M Nordborg, D Weigel, KM Borgwardt, A Korte and DG Grimm
Nucleic Acids Research (NAR), 46.D1 (2018): D1150-D1156 (Link)
- The rate and potential relevance of new mutations in a colonizing plant lineage
M Exposito-Alonso*, C Becker*, VJ Schuenemann, E Reitter, C Setzer, R Slovak, B Brachi, J Hagmann, DG Grimm, C Jiahui, W Busch, J Bergelson, RW Ness, J Krause, HA Burbano and D Weigel
PLoS Genetics, 14.2 (2018): e1007155 (Link)
2017
- Improved EGFR mutation detection using combined exosomal RNA and circulating tumor DNA in NSCLC patient plasma
AK Krug,* D Enderle*, C Karlovich, T Priewasser, S Bentik, A Spiel, K Brinkmann, J Emenegger, DG Grimm, E Castellanos-Rizaldos, JW Goldman, LV Sequist, JC Soria, DR Camidge, SM Gadgeel, HA Wakelee, M Raponi, M Noerholm, J Skog
Annals of Oncology, 29.3 (2017): 700-706. (Link)
- easyGWAS: A cloud-based platform for comparing the results of genome-wide association studies
DG Grimm, D Roqueiro, PA Salome, S Kleeberger, B Greshake, W Zhu, C Liu, C Lippert, O Stegle, B Schölkopf, D Weigel and KM Borgwardt
The Plant Cell, 2017, 29 (1): 5-19 (Link)
- AraPheno: A public database for Arabidopsis thaliana phenotypes
Ü Seren*, DG Grimm*, J Fitz, D Weigel, M Nordborg, KM Borgwardt, A Korte
Nucleic Acids Research (NAR), 2017, 45(D1), D1054-D1059
* Shared first authorship (Link)
2016
- The Genetic Architecture of Non-additive Inheritance in Arabidopsis thaliana Hybrids
DK Seymour*, E Chae*, DG Grimm*, CM Pizzaro, A Haeing-Müller, F Vasseur, B Rakitsch, KM Borgwardt, D Koenig and D Weigel
Proceedings of the National Academy of Sciences (PNAS), 2016, 113 (46), E7317-E7326
* Shared first authorship (Link)
- Epigenomic Diversity in a Global Collection of Arabidopsis thaliana Accessions
Taiji Kawakatsu, Shao-shan Carol Huang, Florian Jupe, Eriko Sasaki, Robert J. Schmitz, Mark A. Urich, Rosa Castanon, Joseph R. Nery, Cesar Barragan, Yupeng He, Huaming Chen, Manu Dubin, Cheng-Ruei Lee, Congmao Wang, Felix Bemm, Claude Becker, Ryan O’Neil, Ronan C. O Malley, Danjuma X. Quarless, The 1001 Genomes Consortium*, Nicholas J. Schork, Detlef Weigel, Magnus Nordborg, Joseph R.
Cell 166.2 (2016): 492-505
* As part of the 1001 Genomes Consortium (Link)
- Genomic profiles of diversification and genotype-phenotype association in island nematode lineages
A McGaughran, C Rödelsperger, DG Grimm, JM Meyer, E Moreno, K Morgan, M Leaver, V Serobyan, B Rakitsch, KM Borgwardt, RJ Sommer
Molecular Biology and Evolution (2016) 33 (9): 2257-2272. (Link)
- 1135 genomes reveal the global pattern of polymorphism in Arabidopsis thaliana
The 1001 Genomes Consortium*
Cell 166.2 (2016): 481-491
* As part of the 1001 Genomes Consortium (Link)
2015
- The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity
DG Grimm, CA Azencott, F Aicheler, U Gieraths, DG MacArthur, KE Samocha, DN Cooper, PD Stenson, MJ Daly, JW Smoller, LE Duncan and KM Borgwardt
Human Mutation 2015, 36(5):513-523 (Link)
- Genome-wide detection of intervals of genetic heterogeneity associated with complex traits
F Llinares-López, DG Grimm, DA Bodenham, U Gieraths, M Sugiyama, B Rowan, KM Borgwardt
Bioinformatics (2015) 31(12):i303-i310 (Link)
- Prediction of human population responses to toxic compounds by a collaborative competition
Eduati F, Mangravite ML, Wang T, Tang H, Bare JC, Huang R, Norman T, Kellen M, Menden PM, Yang J, Zhan X, Zhong R, Xiao G, Xia M, Abdo N, Kosyk O, NIEHS-NCATS-UNC DREAM Toxicogenetics Collaboration*, Friend S, Dearry A, Simeonov A, Tice RR, Rusyn I, Wright FA, Stolovitzky G, Xie Y, Saez-Rodriguez J
Nature Biotechnology 2015 (Link)
* As part of the NIEHS-NCATS-UNC DREAM Toxicogenetics Collaboration
- Genome-wide analysis of local chromatin packing in Arabidopsis thaliana
C Wang, C Liu, D Roqueiro, DG Grimm, R Schwab, C Becker, C Lanz, D Weigel
Genome Research, 2015. 25: 246-256 (Link)
2014
- Multi-task feature selection with multiple networks via maximum flows
M Sugiyama, CA Azencott, DG Grimm, Y Kawahara and K Borgwardt
SIAM International Conference on Data Mining (SDM 2014) (Link)
2013
- Efficient network-guided multi-locus association mapping with graph cuts
CA Azencott, DG Grimm, M Sugiyama, Y Kawahara and K Borgwardt
Bioinformatics 2013, 29(13):i171-i179 (Link)
- Accurate indel prediction using paired-end short reads
DG Grimm*, J Hagmann*, D Koenig, D Weigel and K Borgwardt
BMC Genomics 2013, 14:132 (Link)
* Shared first authorship
- Geometric tree kernels: Classification of COPD from airway tree geometry
A Feragen, J Petersen, DG Grimm, A Dirksen, JH Pedersen, K Borgwardt, M de Bruijne
Information Processing in Medical Imaging (IPMI 2013), Lecture Notes in Computer Science Volume 7917, 171-183 (Link)
2011
- Computational inference of difficult word boundaries in DNA languages
G Tsafnat, P Setzermann, DG Grimm and S Partridge
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL 2011), ACM, New York, USA, 5 pages. (Link)
Books and Book Chapters
Maura John, Dominik Grimm, Arthur Korte (2023)
Predicting Gene Regulatory Interactions Using Natural Genetic Variation
In: Kaufmann, K., Vandepoele, K. (eds) Plant Gene Regulatory Networks.
Methods in Molecular Biology, vol 2698. Humana, New York, NY (Link)
Richa Bharti, Dominik G Grimm (2021)
Design and Analysis of RNA Sequencing Data
In: Melanie Kappelmann-Fenzl. Next Generation Sequencing and Data Analysis.
Learning Materials in Biosciences. Springer, Cham. (Link)
Anja C Gumpinger, Damian Roqueiro, Dominik G Grimm, Karsten M Borgwardt (2018)
Methods and Tools in Genome-Wide Association Studies
In: von Stechow L., Santos Delgado A. (eds) Computational Cell Biology. Methods in Molecular Biology, vol 1819. Humana Press, New York, NY (Link)
Patents
- Johan Skog, Elena Castellanos-Rizaldos, Vasisht Tadigotla, Dominik Grimm, Xuan Zhang, Wei Yu (2018)
Methods and compositions to detect mutations in plasma using exosomal rna and cell free dna from non-small cell lung cancer patients. WO2018102162A1
- Johan Skog, Sudipto Chakrabortty, Dalin Chan, Michael Valentino, Vasisht Tadigotla, Robert Kitchen, Dominik Grimm, Wei Yu (2018)
Sequencing and analysis of exosome associated nucleic acids. WO2018076018A1
