GALAD score detects early hepatocellular carcinoma in an international cohort of patients with nonalcoholic steatohepatitis J Best, LP Bechmann, JP Sowa, S Sydor, A Dechêne, K Pflanz, S Bedreli, ... Clinical Gastroenterology and Hepatology 18 (3), 728-735. e4, 2020 | 261 | 2020 |
DNA-based watermarks using the DNA-Crypt algorithm D Heider, A Barnekow BMC bioinformatics 8 (1), 176, 2007 | 207 | 2007 |
A combination of α-fetoprotein and des-γ-carboxy prothrombin is superior in detection of hepatocellular carcinoma JM Ertle, D Heider, M Wichert, B Keller, R Kueper, P Hilgard, G Gerken, ... Digestion 87 (2), 121-131, 2013 | 176 | 2013 |
Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research F Hufsky, K Lamkiewicz, A Almeida, A Aouacheria, C Arighi, A Bateman, ... Briefings in bioinformatics 22 (2), 642-663, 2021 | 155 | 2021 |
Impact of working memory load on FMRI resting state pattern in subsequent resting phases M Pyka, CF Beckmann, S Schöning, S Hauke, D Heider, H Kugel, V Arolt, ... PloS one 4 (9), e7198, 2009 | 138 | 2009 |
Protistan community analysis: key findings of a large-scale molecular sampling L Grossmann, M Jensen, D Heider, S Jost, E Glücksman, H Hartikainen, ... The ISME journal 10 (9), 2269-2279, 2016 | 135 | 2016 |
The GALAD scoring algorithm based on AFP, AFP-L3, and DCP significantly improves detection of BCLC early stage hepatocellular carcinoma J Best, H Bilgi, D Heider, C Schotten, P Manka, S Bedreli, M Gorray, ... Zeitschrift für Gastroenterologie 54 (12), 1296-1305, 2016 | 127 | 2016 |
Bacterial glycosyltransferases: challenges and opportunities of a highly diverse enzyme class toward tailoring natural products J Schmid, D Heider, NJ Wendel, N Sperl, V Sieber Frontiers in microbiology 7, 182, 2016 | 123 | 2016 |
Prediction of antimicrobial resistance based on whole-genome sequencing and machine learning Y Ren, T Chakraborty, S Doijad, L Falgenhauer, J Falgenhauer, ... Bioinformatics 38 (2), 325-334, 2022 | 113 | 2022 |
The virtual doctor: An interactive clinical-decision-support system based on deep learning for non-invasive prediction of diabetes S Spänig, A Emberger-Klein, JP Sowa, A Canbay, K Menrad, D Heider Artificial Intelligence in Medicine 100, 101706, 2019 | 113 | 2019 |
EFS: an ensemble feature selection tool implemented as R-package and web-application U Neumann, N Genze, D Heider BioData mining 10 (1), 21, 2017 | 109 | 2017 |
Normal liver enzymes are correlated with severity of metabolic syndrome in a large population based cohort J Kälsch, LP Bechmann, D Heider, J Best, P Manka, H Kälsch, JP Sowa, ... Scientific reports 5, 2015 | 97 | 2015 |
Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistance prediction D Heider, R Senge, W Cheng, E Hüllermeier Bioinformatics 29 (16), 1946-1952, 2013 | 92 | 2013 |
DNA watermarks: A proof of concept D Heider, A Barnekow BMC molecular biology 9 (1), 40, 2008 | 90 | 2008 |
Encodings and models for antimicrobial peptide classification for multi-resistant pathogens S Spänig, D Heider BioData Mining 12 (1), 7, 2019 | 85 | 2019 |
Non-invasive assessment of NAFLD as systemic disease—A machine learning perspective A Canbay, J Kälsch, U Neumann, M Rau, S Hohenester, HA Baba, C Rust, ... PloS one 14 (3), e0214436, 2019 | 80 | 2019 |
Novel algorithm for non-invasive assessment of fibrosis in NAFLD JP Sowa, D Heider, LP Bechmann, G Gerken, D Hoffmann, A Canbay PloS one 8 (4), e62439, 2013 | 80 | 2013 |
Privacy-preserving Artificial Intelligence Techniques in Biomedicine R Torkzadehmahani, R Nasirigerdeh, DB Blumenthal, T Kacprowski, ... arXiv preprint arXiv:2007.11621, 2020 | 75 | 2020 |
Prediction of co-receptor usage of HIV-1 from genotype JN Dybowski*, D Heider*, D Hoffmann PLoS computational biology 6 (4), e1000743, 2010 | 72 | 2010 |
Federated Random Forests can improve local performance of predictive models for various healthcare applications AC Hauschild, M Lemanczyk, J Matschinske, T Frisch, O Zolotareva, ... Bioinformatics 38 (8), 2278-2286, 2022 | 67 | 2022 |