| The synaptic ribbon is critical for sound encoding at high rates and with temporal precision P Jean, D Lopez de la Morena, S Michanski, LM Jaime Tobón, ... Elife 7, e29275, 2018 | 101 | 2018 |
| Detecting animal contacts—A deep learning-based pig detection and tracking approach for the quantification of social contacts M Wutke, F Heinrich, PP Das, A Lange, M Gentz, I Traulsen, FK Warns, ... Sensors 21 (22), 7512, 2021 | 39 | 2021 |
| Identification of candidate signature genes and key regulators associated with Trypanotolerance in the Sheko Breed YA Mekonnen, M Gültas, K Effa, O Hanotte, AO Schmitt Frontiers in genetics 10, 1095, 2019 | 32 | 2019 |
| PC-TraFF: identification of potentially collaborating transcription factors using pointwise mutual information C Meckbach, R Tacke, X Hua, S Waack, E Wingender, M Gültas BMC bioinformatics 16, 1-21, 2015 | 28 | 2015 |
| The Sponge Genetree Server providing a phylogenetic backbone for poriferan evolutionary studies D Erpenbeck, O Voigt, M Gueltas, G Woerheide Zootaxa 1939 (1), 58–60-58–60, 2008 | 25 | 2008 |
| Identification of Regulatory SNPs Associated with Vicine and Convicine Content of Vicia faba Based on Genotyping by Sequencing Data Using Deep Learning F Heinrich, M Wutke, PP Das, M Kamp, M Gültas, W Link, AO Schmitt Genes 11 (6), 614, 2020 | 24 | 2020 |
| CRF-based models of protein surfaces improve protein-protein interaction site predictions Z Dong, K Wang, TK Linh Dang, M Gültas, M Welter, T Wierschin, ... BMC bioinformatics 15, 1-14, 2014 | 23 | 2014 |
| Unravelling the Complex Interplay of Transcription Factors Orchestrating Seed Oil Content in Brassica napus L. A Rajavel, S Klees, JS Schlüter, H Bertram, K Lu, AO Schmitt, M Gültas International journal of molecular sciences 22 (3), 1033, 2021 | 21 | 2021 |
| Identification of Age-Specific and Common Key Regulatory Mechanisms Governing Eggshell Strength in Chicken Using Random Forests F Ramzan, S Klees, AO Schmit, D Cavero, M Gültas Genes 2020, 11(4), 464; https://doi.org/10.3390/genes11040464, 2020 | 21 | 2020 |
| Computational identification of tissue-specific transcription factor cooperation in ten cattle tissues L Steuernagel, C Meckbach, F Heinrich, S Zeidler, AO Schmitt, M Gültas PLOS ONE, 2019 | 21 | 2019 |
| Deep learning the cis-regulatory code for gene expression in selected model plants FF Peleke, SM Zumkeller, M Gültas, A Schmitt, J Szymański Nature Communications 15 (1), 3488, 2024 | 20 | 2024 |
| Investigation of Pig Activity Based on Video Data and Semi-Supervised Neural Networks M Wutke, AO Schmitt, I Traulsen, M Gültas AgriEngineering, 2020 | 20 | 2020 |
| Optogenetics and electron tomography for structure-function analysis of cochlear ribbon synapses R Chakrabarti, LMJ Tobón, L Slitin, MR Canales, G Hoch, M Slashcheva, ... Elife 11, e79494, 2022 | 19 | 2022 |
| In Silico Identification of the Complex Interplay between Regulatory SNPs, Transcription Factors, and Their Related Genes in Brassica napus L. Using Multi-Omics Data S Klees, TM Lange, H Bertram, A Rajavel, JS Schlüter, K Lu, AO Schmitt, ... International Journal of Molecular Sciences 22 (2), 789, 2021 | 18 | 2021 |
| Combining random forests and a signal detection method leads to the robust detection of genotype-phenotype associations F Ramzan, M Gültas, H Bertram, D Cavero, AO Schmitt Genes 11 (8), 892, 2020 | 17 | 2020 |
| Computational detection of stage-specific transcription factor clusters during heart development S Zeidler, C Meckbach, R Tacke, FS Raad, A Roa, S Uchida, ... Frontiers in Genetics 7, 33, 2016 | 15 | 2016 |
| Computational identification of key regulators in two different colorectal cancer cell lines D Wlochowitz, M Haubrock, J Arackal, A Bleckmann, A Wolff, T Beißbarth, ... Frontiers in genetics 7, 42, 2016 | 14 | 2016 |
| Quantum coupled mutation finder: predicting functionally or structurally important sites in proteins using quantum Jensen-Shannon divergence and CUDA programming M Gültas, G Düzgün, S Herzog, SJ Jäger, C Meckbach, E Wingender, ... BMC bioinformatics 15, 1-17, 2014 | 14 | 2014 |
| Breeding objectives and selection criteria for four strains of Pakistani Beetal goats identified in a participatory approach F Ramzan, MS Khan, SA Bhatti, M Gültas, AO Schmitt Small Ruminant Research 190, 106163, 2020 | 13 | 2020 |
| Identifying Cattle Breed-Specific Partner Choice of Transcription Factors during the African Trypanosomiasis Disease Progression Using Bioinformatics Analysis A Rajavel, F Heinrich, AO Schmit, M Gültas Vaccines, https://www.mdpi.com/2076-393X/8/2/246, 2020 | 13 | 2020 |
Stephan WaackProfessor für Informatik, Universität GöttingenEmail yang diverifikasi di informatik.uni-goettingen.de