Peter Meinicke
Peter Meinicke
Senior Scientist, University of Goettingen
Verified email at - Homepage
Cited by
Cited by
Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data
KP Aßhauer, B Wemheuer, R Daniel, P Meinicke
Bioinformatics 31 (17), 2882-2884, 2015
Critical assessment of metagenome interpretation—a benchmark of metagenomics software
A Sczyrba, P Hofmann, P Belmann, D Koslicki, S Janssen, J Dröge, ...
Nature methods 14 (11), 1063-1071, 2017
Critical assessment of metagenome interpretation—a benchmark of metagenomics software
A Sczyrba, P Hofmann, P Belmann, D Koslicki, S Janssen, J Dröge, ...
Nature methods 14 (11), 1063-1071, 2017
BCI competition 2003-data set IIb: support vector machines for the P300 speller paradigm
M Kaper, P Meinicke, U Grossekathoefer, T Lingner, H Ritter
IEEE Transactions on biomedical Engineering 51 (6), 1073-1076, 2004
Metabolic priming by a secreted fungal effector
A Djamei, K Schipper, F Rabe, A Ghosh, V Vincon, J Kahnt, S Osorio, ...
Nature 478 (7369), 395-398, 2011
Score-based prediction of genomic islands in prokaryotic genomes using hidden Markov models
S Waack, O Keller, R Asper, T Brodag, C Damm, WF Fricke, K Surovcik, ...
BMC bioinformatics 7 (1), 1-12, 2006
Tax4Fun2: prediction of habitat-specific functional profiles and functional redundancy based on 16S rRNA gene sequences
F Wemheuer, JA Taylor, R Daniel, E Johnston, P Meinicke, T Thomas, ...
Environmental Microbiome 15 (1), 1-12, 2020
Orphelia: predicting genes in metagenomic sequencing reads
KJ Hoff, T Lingner, P Meinicke, M Tech
Nucleic acids research 37 (suppl_2), W101-W105, 2009
Improving transfer rates in brain computer interfacing: a case study
P Meinicke, M Kaper, F Hoppe, M Heumann, H Ritter
Advances in Neural Information Processing Systems 15, 2002
Identification of novel plant peroxisomal targeting signals by a combination of machine learning methods and in vivo subcellular targeting analyses
T Lingner, AR Kataya, GE Antonicelli, A Benichou, K Nilssen, XY Chen, ...
The Plant Cell 23 (4), 1556-1572, 2011
Gene prediction in metagenomic fragments: a large scale machine learning approach
KJ Hoff, M Tech, T Lingner, R Daniel, B Morgenstern, P Meinicke
BMC bioinformatics 9, 1-14, 2008
The COP9 signalosome mediates transcriptional and metabolic response to hormones, oxidative stress protection and cell wall rearrangement during fungal development
K Nahlik, M Dumkow, Ö Bayram, K Helmstaedt, S Busch, O Valerius, ...
Molecular microbiology 78 (4), 964-979, 2010
Fungal soil communities in a young transgenic poplar plantation form a rich reservoir for fungal root communities
L Danielsen, A Thürmer, P Meinicke, M Buee, E Morin, F Martin, G Pilate, ...
Ecology and Evolution 2 (8), 1935-1948, 2012
MarVis-Pathway: integrative and exploratory pathway analysis of non-targeted metabolomics data
A Kaever, M Landesfeind, K Feussner, A Mosblech, I Heilmann, ...
Metabolomics 11, 764-777, 2015
UProC: tools for ultra-fast protein domain classification
P Meinicke
Bioinformatics 31 (9), 1382-1388, 2015
Sonifications for EEG data analysis
T Hermann, P Meinicke, H Bekel, H Ritter, HM Müller, S Weiss
Georgia Institute of Technology, 2002
Principal surfaces from unsupervised kernel regression
P Meinicke, S Klanke, R Memisevic, H Ritter
IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (9), 1379-1391, 2005
Mixture models for analysis of the taxonomic composition of metagenomes
P Meinicke, KP Aßhauer, T Lingner
Bioinformatics 27 (12), 1618-1624, 2011
Remote homology detection based on oligomer distances
T Lingner, P Meinicke
Bioinformatics 22 (18), 2224-2231, 2006
Oligo kernels for datamining on biological sequences: a case study on prokaryotic translation initiation sites
P Meinicke, M Tech, B Morgenstern, R Merkl
BMC bioinformatics 5 (1), 1-14, 2004
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