Jens Keilwagen
Jens Keilwagen
Julius Kuehn-Institute
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Cited by
Cited by
Shifting the limits in wheat research and breeding using a fully annotated reference genome
R Appels, K Eversole, N Stein, C Feuillet, B Keller, J Rogers, CJ Pozniak, ...
Science 361 (6403), 2018
Critical assessment of automated flow cytometry data analysis techniques
N Aghaeepour, G Finak, H Hoos, TR Mosmann, R Brinkman, R Gottardo, ...
Nature methods 10 (3), 228-238, 2013
Evaluation of methods for modeling transcription factor sequence specificity
MT Weirauch, A Cote, R Norel, M Annala, Y Zhao, TR Riley, ...
Nature biotechnology 31 (2), 126-134, 2013
PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R
J Grau, I Grosse, J Keilwagen
Bioinformatics 31 (15), 2595-2597, 2015
Toward the identification and regulation of the Arabidopsis thaliana ABI3 regulon
G Mönke, M Seifert, J Keilwagen, M Mohr, I Grosse, U Hähnel, A Junker, ...
Nucleic acids research 40 (17), 8240-8254, 2012
Using intron position conservation for homology-based gene prediction
J Keilwagen, M Wenk, JL Erickson, MH Schattat, J Grau, F Hartung
Nucleic acids research 44 (9), e89-e89, 2016
Elongation‐related functions of LEAFY COTYLEDON1 during the development of Arabidopsis thaliana
A Junker, G Mönke, T Rutten, J Keilwagen, M Seifert, TMN Thi, JP Renou, ...
The Plant Journal 71 (3), 427-442, 2012
Area under Precision-Recall Curves for Weighted and Unweighted Data
J Keilwagen, I Grosse, J Grau
PLOS ONE 9 (3), e92209, 2014
De-novo discovery of differentially abundant transcription factor binding sites including their positional preference
J Keilwagen, J Grau, IA Paponov, S Posch, M Strickert, I Grosse
PLoS Comput Biol 7 (2), e1001070, 2011
Varying levels of complexity in transcription factor binding motifs
J Keilwagen, J Grau
Nucleic acids research 43 (18), e119-e119, 2015
A general approach for discriminative de novo motif discovery from high-throughput data
J Grau, S Posch, I Grosse, J Keilwagen
Nucleic acids research 41 (21), e197-e197, 2013
Jstacs: a Java framework for statistical analysis and classification of biological sequences
J Grau, J Keilwagen, A Gohr, B Haldemann, S Posch, I Grosse
The Journal of Machine Learning Research 13 (1), 1967-1971, 2012
Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi
J Keilwagen, F Hartung, M Paulini, SO Twardziok, J Grau
BMC bioinformatics 19 (1), 189, 2018
Exact algorithms and heuristics for the Quadratic Traveling Salesman Problem with an application in bioinformatics
A Fischer, F Fischer, G Jäger, J Keilwagen, P Molitor, I Grosse
Discrete Applied Mathematics 166, 97-114, 2014
The Terpene Synthase Gene Family of Carrot (Daucus carota L.): Identification of QTLs and Candidate Genes Associated with Terpenoid Volatile Compounds
J Keilwagen, H Lehnert, T Berner, H Budahn, T Nothnagel, D Ulrich, ...
Frontiers in Plant Science 8, 1930, 2017
Separating the wheat from the chaff – a strategy to utilize plant genetic resources from ex situ genebanks
J Keilwagen, B Kilian, H Özkan, S Babben, D Perovic, KFX Mayer, ...
Scientific reports 4, 5231, 2014
Accurate prediction of cell type-specific transcription factor binding
J Keilwagen, S Posch, J Grau
Genome Biology 20 (1), 9, 2019
Mineralocorticoid receptor interaction with SP1 generates a new response element for pathophysiologically relevant gene expression
S Meinel, S Ruhs, K Schumann, N Strätz, K Trenkmann, B Schreier, ...
Nucleic acids research 41 (17), 8045-8060, 2013
DiffLogo: a comparative visualization of sequence motifs
M Nettling, H Treutler, J Grau, J Keilwagen, S Posch, I Grosse
BMC bioinformatics 16 (1), 1-9, 2015
Detection and identification of genome editing in plants: challenges and opportunities
L Grohmann, J Keilwagen, N Duensing, E Dagand, F Hartung, R Wilhelm, ...
Frontiers in Plant Science 10, 236, 2019
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