Publications

Source: https://bit.cs.tum.de/en/publications Parent: https://bit.cs.tum.de/en/

An up-to-date list of publications of Prof. Dr. Dominik Grimm can be found on Google Scholar or on ResearchGate.

PEER REVIEWED ARTICLES

2025

• Identification of Pressure-Swing Separation Processes for Azeotropic Mixtures Using Deep Reinforcement Learning\ WB Wolf, J Pirnay, Q Göttl, DG Grimm, J Burger\ Chemie Ingenieur Technik, 2025 (https://doi.org/10.1002/cite.70032)\
• Forecasting sales and identifying purchase-driving attributes of perishable products with explainable artificial intelligence based on the example of yogurt \ K Brückner*, J Eiglsperger*, A Emberger-Klein, DG Grimm, K Menrad\ British Food Journal, 2025 (in press)\
• GraphXForm: Graph transformer for computer-aided molecular design \ J Pirnay**, JG Rittig*, WB Wolf*, M Grohe, J Burger, A Mitsos, DG Grimm\ *Digital Discovery, 2025 (https://doi.org/10.1039/D4DD00339J) [Code]\
• The combined effect of decreased stomatal density and aperture increases water use efficiency in maize \ L Barl, BD Benato, N Genze, DG Grimm, M Gigl, C Dawid, CC Schön, V Avramova\ Scientific Reports, 2025 (https://doi.org/10.1038/s41598-025-94833-1) [Code]\
• Precurement and production planning in horticulture considering short-term re-order opportunities  \ M Drechsler, J Eiglsperger,  DG Grimm, A Holzapfel\ International Journal of Production Economics, 2025 (https://doi.org/10.1016/j.ijpe.2025.109583)\
• 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**, 2025 (https://doi.org/10.1016/j.compchemeng.2024.108975) [Code]

2024

• 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 & TMLR to ICLR 2025 Event 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