Publications 2022
[1] Schuhmacher, D., Schörner, S., Küpper, C., Großerueschkamp, F., Sternemann, C., Lugnier, C., ... & Mosig, A. (2021). A Framework for Falsifiable Explanations of Machine Learning Models with an Application in Computational Pathology. medRxiv.
[2] Blum, J., Masjosthusmann, S., Bartmann, K., Bendt, F., Dolde, X., Dönmez, A., ... & Fritsche, E. (2022). Establishment of a human cell-based in vitro battery to assess developmental neurotoxicity hazard of chemicals. Chemosphere, 137035.
[3] Arto, T., Grosserueschkamp, F., Mueller, T. M., Mosig, A., Neurath, M. F., Zundler, S., & Gerwert, K. (2022, September). Differential diagnosis of Crohn's disease and Ulcerative Colitis with deep learning based on hyperspectral infrared images. In VIRCHOWS ARCHIV (Vol. 481, No. SUPPL 1, pp. S78-S78). ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES: SPRINGER.
[4] Brandenburg, V. B., Narberhaus, F., & Mosig, A. (2022). Inverse folding based pre-training for the reliable identification of intrinsic transcription terminators. PLOS Computational Biology, 18(7), e1010240.
[5] Gerwert, K., Schörner, S., Großerueschkamp, F., Kraeft, A., Schuhmacher, D., Lugnier, C., ... & Reinacher-Schick, A. (2022). PD-11 In depth analysis of label-free infrared (IR) imaging-based microsatellite instability (MSI) classification in early colon cancer (CC) on samples from the AIO ColoPredictPlus 2.0 (CPP) registry trial. Annals of Oncology, 33, S243.
[6] Schörner, S., Großerueschkamp, F., Kraeft, A. L., Schuhmacher, D., Sternemann, C. W., Feder, I. S., ... & Reinacher-Schick, A. C. (2022). Label-free and automated approach to rapidly classify microsatellite instability (MSI) in early colon cancer (CC) analyzing the AIO ColoPredictPlus 2.0 (CPP) registry trial.
[7] Förster, N., Butke, J., Keßel, H. E., Bendt, F., Pahl, M., Li, L., ... & Mosig, A. (2022). Reliable identification and quantification of neural cells in microscopic images of neurospheres. Cytometry Part A, 101(5), 411-422.
[8] Jelonek, M., Raulf, A. P., Fiala, E., Butke, J., Herrmann, T., & Mosig, A. (2022). A formative usability study of workflow management systems in label-free digital pathology. F1000Research, 11(192), 192.
[9] Kessel, H. E., Masjosthusmann, S., Bartmann, K., Blum, J., Doenmez, A., Foerster, N., ... & Fritsche, E. (2022). Biostatistics and its impact on hazard characterization using in vitro developmental neurotoxicity assays. bioRxiv.
[10] Spangenberg, P., Hagemann, N., Squire, A., Foerster, N., Krauss, S. D., Qi, Y., ... & Mosig, A. (2022). Rapid and fully automated blood vasculature analysis in 3D light-sheet image volumes of different organs. bioRxiv.
[11] Jelonek, M., Raulf, A. P., Fiala, E., Butke, J., Herrmann, T., & Mosig, A. (2022). A formative usability study of workflow managemen t systems in label-free digital pathology [version 1; peer review.
Publications 2021
[1] Gösser, F., Raulf, A., Mosig, A., Tollrian, R., & Schweinsberg, M. (2021). Signaling pathways of heat-and hypersalinity-induced polyp bailout in Pocillopora acuta. Coral Reefs, 40(6), 1713-1728.
[2] Selbach, L., Kowalski, T., Gerwert, K., Buchin, M., & Mosig, A. (2021). Shape decomposition algorithms for laser capture microdissection. Algorithms for Molecular Biology, 16(1), 1-17.
[3] Butke, J., Frick, T., Roghmann, F., El-Mashtoly, S. F., Gerwert, K., & Mosig, A. (2021, September). End-to-end Multiple Instance Learning for Whole-Slide Cytopathology of Urothelial Carcinoma. In MICCAI Workshop on Computational Pathology (pp. 57-68). PMLR.
[4] Großerüschkamp, F., Schörner, S. M., Kraeft, A. L., Schuhmacher, D., Sternemann, C., Jütte, H., ... & Tannapfel, A. (2021). 385O Automated detection of microsatellite status in early colon cancer (CC) using artificial intelligence (AI) integrated infrared (IR) imaging on unstained samples from the AIO ColoPredictPlus 2.0 (CPP) registry study. Annals of Oncology, 32, S531-S532.
[5] Schikora, J., Kiwatrowski, N., Förster, N., Selbach, L., Ostendorf, F., Pallapies, F., ... & Tönges, L. (2021). A Propagated Skeleton Approach to High Throughput Screening of Neurite Outgrowth for In Vitro Parkinson’s Disease Modelling. Cells, 10(4), 931.
[6] Raulf, A. P., Butke, J., Menzen, L., Küpper, C., Großerueschkamp, F., Gerwert, K., & Mosig, A. (2021). A representation learning approach for recovering scatter‐corrected spectra from Fourier‐transform infrared spectra of tissue samples. Journal of Biophotonics, 14(3), e202000385.
[7] Raulf, A. P., Däubener, S., Hack, B., Mosig, A., & Fischer, A. (2021). SmoothLRP: Smoothing LRP by Averaging over Stochastic Input Variations. In ESANN.
Publications 2020
[1] Masjosthusmann, S., Blum, J., Bartmann, K., Dolde, X., Holzer, A. K., Stürzl, L. C., ... & Fritsche, E. (2020). Establishment of an a priori protocol for the implementation and interpretation of an in‐vitro testing battery for the assessment of developmental neurotoxicity. EFSA Supporting Publications, 17(10), 1938E.
[2] Mosig, A. (2020). Letter to the Editor regarding the recent contribution by Roussel et al., SARS-CoV-2: Fear versus data. International journal of antimicrobial agents, 56(4), 106074.
[3] Raulf, A. P., Hack, B. L., Däubener, S., Mosig, A., & Fischer, A. (2020). SmoothLRP: Smoothing Explanations of Neural Network Decisions by Averaging over Stochastic Input Variations.
[4] Trukhan, S., Tafintseva, V., Tøndel, K., Großerueschkamp, F., Mosig, A., Kovalev, V., ... & Kohler, A. (2020). Grayscale representation of infrared microscopy images by extended multiplicative signal correction for registration with histological images. Journal of Biophotonics, 13(8), e201960223.
[5] Twittenhoff, C., Brandenburg, V. B., Righetti, F., Nuss, A. M., Mosig, A., Dersch, P., & Narberhaus, F. (2020). Lead-seq: transcriptome-wide structure probing in vivo using lead (II) ions. Nucleic acids research, 48(12), e71-e71.
[6] Raulf, A. P., Butke, J., Menzen, L., Küpper, C., Großerueschkamp, F., Gerwert, K., & Mosig, A. (2020). Deep Neural Networks for the Correction of Mie Scattering in Fourier-Transformed Infrared Spectra of Biological Samples. arXiv preprint arXiv:2002.07681.
[7] Schuhmacher, D., Gerwert, K., & Mosig, A. (2020). A generic neural network approach to infer segmenting classifiers for disease-associated regions in medical images. medRxiv.
[8] Raulf, A. P., Butke, J., Küpper, C., Großerueschkamp, F., Gerwert, K., & Mosig, A. (2020). Deep representation learning for domain adaptable classification of infrared spectral imaging data. Bioinformatics, 36(1), 287-294.
Publications 2019
[1] Raulf, A. P., Butke, J., Küpper, C., Großerueschkamp, F., Gerwert, K., & Mosig, A. (2019). Deep representation learning for domain adaptatable classification of infrared spectral imaging data. bioRxiv, 584227.
[2] Buchin, M., Mosig, A., & Selbach, L. Skeleton-based decomposition of simple polygons. EuroCG 2019, Utrecht.
[3] Schuhmacher, D., Gerwert, K., Mosig, A. A Generic Neural Network Approach to Infer Segmenting Classifiers for Disease-Associated Regions in Medical Image data. submitted. Compay2019
Publications 2018
[1] Krauß, S. D., Roy, R., Yosef, H. K., Lechtonen, T., El‐Mashtoly, S. F., Gerwert, K., & Mosig, A. (2018). Inside Cover: Hierarchical deep convolutional neural networks combine spectral and spatial information for highly accurate Raman‐microscopy‐based cytopathology (J. Biophotonics 10/2018). Journal of Biophotonics, 11(10), e201870163.
[2] Krauß, S. D., Roy, R., Yosef, H. K., Lechtonen, T., El‐Mashtoly, S. F., Gerwert, K., & Mosig, A. (2018). Hierarchical deep convolutional neural networks combine spectral and spatial information for highly accurate Raman‐microscopy‐based cytopathology. Journal of biophotonics, 11(10), e201800022.
Publications 2017
[1] Großerueschkamp, F., Bracht, T., Diehl, H. C., Kuepper, C., Ahrens, M., Kallenbach-Thieltges, A., ... & Brüning, T. (2017). Spatial and molecular resolution of diffuse malignant mesothelioma heterogeneity by integrating label-free FTIR imaging, laser capture microdissection and proteomics. Scientific Reports, 7.
[2] Yosef, H. K., Krauß, S. D., Lechtonen, T., Jütte, H., Tannapfel, A., Käfferlein, H. U., ... & Gerwert, K. (2017). Non-invasive diagnosis of high-grade urothelial carcinoma in urine by Raman spectral imaging. Analytical Chemistry, 89 (12), 6893–6899.
[3] Krauß, S. D., Yosef, H. K., Lechtonen, T., Jütte, H., Tannapfel, A., Käfferlein, H. U., ... & Gerwert, K. Integrating spatial, morphological, and textural information for improved cell type differentiation using Raman microscopy. Journal of Chemometrics.
Publications 2016
[1] Schmuck, M. R., Temme, T., Dach, K., de Boer, D., Barenys, M., Bendt, F., ... & Fritsche, E. (2016). Omnisphero: a high-content image analysis (HCA) approach for phenotypic developmental neurotoxicity (DNT) screenings of organoid neurosphere cultures in vitro. Archives of Toxicology, 1-12.
Publications 2015
[1] Sascha D Krauß, Dennis Petersen, Daniel Niedieker, Inka Fricke, Erik Freier, Samir F El-Mashtoly, Klaus Gerwert, and Axel Mosig. Colocalization of fluorescence and raman microscopic images for the identification of subcellular compartments: a validation study. Analyst, 2015.
[2] Xiaodong Qi, Dustin P Rand, Joshua D Podlevsky, Yang Li, Axel Mosig, Peter F Stadler, and Julian J-L Chen. Prevalent and distinct spliceosomal 3-end processing mechanisms for fungal telomerase rna. Nature communications, 6, 2015.
[3] Yang, C., Niedieker, D., Großerüschkamp, F., Horn, M., Tannapfel, A., Kallenbach-Thieltges, A., ... & Mosig, A. (2015). Fully automated registration of vibrational microspectroscopic images in histologically stained tissue sections. BMC bioinformatics, 16(1), 396.
Publications 2014
[1] Samir F El-Mashtoly, Daniel Niedieker, Dennis Petersen, Sascha D Krauss, Erik Freier, Abdelouahid Maghnouj, Axel Mosig, Stephan Hahn, Carsten Kötting, and Klaus Gerwert. Automated identification of subcellular organelles by coherent anti-stokes raman scattering. Biophysical journal, 106(9):1910–1920, 2014.
[2] Samir F El-Mashtoly, Dennis Petersen, Hesham K Yosef, Axel Mosig, Anke Reinacher-Schick, Carsten Kötting, and Klaus Gerwert. Label-free imaging of drug distribution and metabolism in colon cancer cells by raman microscopy. Analyst, 139(5):1155–1161, 2014.
[3] Martin Schmuck, Thomas Temme, Sabrina Heinz, Christine Baksmeier, Axel Mosig, M Teresa Colomina, Marta Barenys, and Ellen Fritsche. Automatic counting and positioning of 5-bromo-2-deoxyuridine (brdu) positive cells in cortical layers of rat brain slices. Neurotoxicology, 2014.
Publications 2013
[1] Angela Kallenbach-Thieltges, Frederik Großerüschkamp, Axel Mosig, Max Diem, Andrea Tannapfel, and Klaus Gerwert. Immunohistochemistry, histopathology and infrared spectral histopathology of colon cancer tissue sections. Journal of biophotonics, 6(1):88–100, 2013.
[2] Laven Mavarani, Dennis Petersen, Samir F El-Mashtoly, Axel Mosig, Andrea Tannapfel, Carsten Kötting, and Klaus Gerwert. Spectral histopathology of colon cancer tissue sections by raman imaging with 532 nm excitation provides label free annotation of lymphocytes, erythrocytes and proliferating nuclei of cancer cells. Analyst, 138(14):4035–4039, 2013.
[3] Chaofeng Wang, Cai-Ping Gui, Hai-Kuan Liu, Dong Zhang, and Axel Mosig. An image skeletonization-based tool for pollen tube morphology analysis and phenotypingf. Journal of integrative plant biology, 55(2):131–141, 2013.
[4] Qiaoyong Zhong, Chen Yang, Frederik Großerüschkamp, Angela Kallenbach-Thieltges, Peter Serocka, Klaus Gerwert, and Axel Mosig. Similarity maps and hierarchical clustering for annotating ft-ir spectral images. BMC bioinformatics, 14(1):333, 2013.
Publications 2012
[1] Xiaodong Qi, Yang Li, Shinji Honda, Steve Hoffmann, Manja Marz, Axel Mosig, Joshua D Podlevsky, Peter F Stadler, Eric U Selker, and Julian J-L Chen. The common ancestral core of vertebrate and fungal telomerase rnas. Nucleic acids research, 2012.
Publications 2011
[1] Yan Li, Jin Guo, Chaofeng Wang, Zhichao Fan, Guangda Liu, Cheng Wang, Zhengqin Gu, David Damm, Axel Mosig, and Xunbin Wei. Circulation times of prostate cancer and hepatocellular carcinoma cells by in vivo flow cytometry. Cytometry Part A, 79(10):848–854, 2011.
[2] Axel Mosig and Peter F Stadler. Evolution of vault rnas. eLS, 2011.
[3] Vivek Thakur, Samart Wanchana, Mercedes Xu, Richard Bruskiewich, William P Quick, Axel Mosig, and Xin-Guang Zhu. Characterization of statistical features for plant microrna prediction. BMC genomics, 12(1):108, 2011.
[4] Hang Xiao, Ying Li, Jiulin Du, and Axel Mosig. Ct3d: tracking microglia motility in 3d using a novel cosegmentation approach. Bioinformatics, 27(4):564–571, 2011.
[5] Hang Xiao, Melvin Zhang, Axel Mosig, and Hon Wai Leong. Dynamic programming algorithms for efficiently computing cosegmentations between biological images. In Algorithms in Bioinformatics, pages 339–350. Springer Berlin Heidelberg, 2011.
Publications 2010
[1] Guofeng Meng, Axel Mosig, and Martin Vingron. A computational evaluation of over-representation of regulatory motifs in the promoter regions of differentially expressed genes. BMC bioinformatics, 11(1):267, 2010.