Publications

Book Chapters, Workshop Proceedings, Abstracts and Patents can be found after the peer reviewed articles.

PEER REVIEWED ARTICLES

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

• 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, Preprint on biorxiv:2022.04.05.487185v1) [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)

Abstracts, Workshop Proceedings & Posters

• Automatisierte Fließbildsynthese durch Reinforcement Learning
  Q Göttl, DG Grimm, J Burger
  10. ProcessNet‐Jahrestagung und 34. DECHEMA‐Jahrestagung der Biotechnologen 2020: Processes for Future, 2020

• MiCroM: A Comprehensive Pipeline for Gene Amplicon and Metagenomic Data Analysis
  Bharti R., Grimm, D. G.
  German Conference on Bioinformatics (GCB), 2019

• MiCroM: A Comprehensive Pipeline for Gene Amplicon and Metagenomic Data Analysis
  Bharti R., Grimm, D. G.
  27th Conference on Intelligent Systems for Molecular Biology (ISMB), 2019

• Hybrid heuristic for the patient-bed allocation problem.
  Schäfer F., Hübner A., Grimm, D. G.
  European Conference on Operational Research (EURO), 2019 (Link)

• Exosome-based detection of EGFR T790M in plasma from non-small cell lung cancer patients.
  Castellanos-Rizaldos, E., Grimm, D. G., Tadigotla, V., Hurley, J., Healy, J., Neal, P. L., … & Krug, A. K..
  Clinical Cancer Research, 2018 (Link)

• easyGWAS: A cloud-based platform for comparing the results of genome-wide association studies
  Dominik G. Grimm, Damian Roqueiro, Matteo Togninalli, easyGWAS Consortium and Karsten Borgwardt
  Intelligent Systems for Molecular Biology (ISMB), Technology Track Presentation, 2017 (Link, Abstract)

• Long RNA sequencing of human plasma exosomes reveals full coverage of diverse protein coding and long non coding RNA.
  Chakrabortty, S. K., Bedford, L., Uchiyama, H., Tadigotla, V., Valentino, M. D., Grimm, D., … & Skog, J. 
  Cancer Research, 2017 (Link)

• Plasma EGFR T790M mutation detection in NSCLC patients using a combined exosomal RNA and circulating tumor DNA qPCR assay.
  Castellanos-Rizaldos, E., Grimm, D. G., Tadigotla, V., Bentink, S., Hurley, J., Healy, J., … & Karlovich, C.
  European Journal of Cancer, 69, S3., 2016 (Link)

• Beware of circularity: A critical assessment of the state of the art in deleteriousness prediction of missense variants.
  Chloé-Agathe Azencott, Dominik G Grimm, Jordan Smoller and Laramie Duncan and Karsten M. Borgwardt.
  64th Annual Meeting of The American Society of Human Genetics, 2014

• Efficiently mapping phenotypes to networks of genetic loci.
  Chloé-Agathe Azencott, Dominik G Grimm, Yoshinobu Kawahara and Karsten M. Borgwardt.
  NIPS Workshop, Machine Learning in Computational Biology Workshop, 2012

• easyGWAS: A central resource for efficient performance of genome-wide association studies
  Dominik Grimm, Bastian Greshake, Oliver Stegle, Christoph Lippert, Bernhard Schölkopf, Detlef Weigel, Karsten Borgwardt
  International PhD Program in the Biological Sciences, Max Planck Institute for Developmental Biology, Germany (2012).

• Support Vector Machines for Finding Deletions and Short Insertions Using Paired-End Short Reads.
  Grimm, D. G., Hagmann, J., Koenig, D., Weigel, D., & Borgwardt, K.
  Machine Learning Sommer School, MLSS, 2011 (Link)

• Support Vector Machines for finding deletions and short insertions using paired-end short reads.
  Grimm, D. G., Hagmann, J., Koenig, D., Weigel, D., & Borgwardt, K.
  Intelligent Systems for Molecular Biology (ISMB), 2011 (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