Sebastian J. Schultheiss
Sebastian J. Schultheiss
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Cited by
Cited by
Multiple reference genomes and transcriptomes for Arabidopsis thaliana
X Gan, O Stegle, J Behr, JG Steffen, P Drewe, KL Hildebrand, R Lyngsoe, ...
Nature 477 (7365), 419-423, 2011
Hormonal control of the shoot stem-cell niche
Z Zhao, SU Andersen, K Ljung, K Dolezal, A Miotk, SJ Schultheiss, ...
Nature 465 (7301), 1089-1092, 2010
De novo assembly of the cattle reference genome with single-molecule sequencing
BD Rosen, DM Bickhart, RD Schnabel, S Koren, CG Elsik, E Tseng, ...
Gigascience 9 (3), giaa021, 2020
Transcriptional control of a plant stem cell niche
W Busch, A Miotk, FD Ariel, Z Zhao, J Forner, G Daum, T Suzaki, ...
Developmental cell 18 (5), 841-853, 2010
Modernizing the bovine reference genome assembly
BD Rosen, DM Bickhart, RD Schnabel, S Koren, CG Elsik, A Zimin, ...
Proceedings of the 11th world congress on genetics applied to livestock …, 2018
KIRMES: kernel-based identification of regulatory modules in euchromatic sequences
SJ Schultheiss, W Busch, JU Lohmann, O Kohlbacher, G Rätsch
Bioinformatics 25 (16), 2126-2133, 2009
KIRMES: kernel-based identification of regulatory modules in euchromatic sequences
S Schultheiss, W Busch, J Lohmann, O Kohlbacher, G Rätsch
BMC Bioinformatics 10 (Suppl 13), O1, 2009
Persistence and availability of Web services in computational biology
SJ Schultheiss, MC Münch, GD Andreeva, G Rätsch
PLoS One 6 (9), e24914, 2011
Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops
N Genze, R Bharti, M Grieb, SJ Schultheiss, DG Grimm
Plant methods 16 (1), 1-11, 2020
Oqtans: the RNA-seq workbench in the cloud for complete and reproducible quantitative transcriptome analysis
VT Sreedharan, SJ Schultheiss, G Jean, A Kahles, R Bohnert, P Drewe, ...
Bioinformatics 30 (9), 1300-1301, 2014
Ten simple rules for providing a scientific web resource
SJ Schultheiss
PLoS Computational Biology 7 (5), e1001126, 2011
Oqtans: a Galaxy-integrated workflow for quantitative transcriptome analysis from NGS Data
SJ Schultheiss, G Jean, J Behr, R Bohnert, P Drewe, N Görnitz, A Kahles, ...
BMC Bioinformatics 12 (11), 1-2, 2011
Kernel-based identification of regulatory modules
SJ Schultheiss
Computational Biology of Transcription Factor Binding, 213-223, 2010
Approaches taken, progress made, and enhanced utility of long read-based goat, swine, cattle and sheep reference genomes
TPL Smith, S Koren, A Phillippy, DM Bickhart, BD Rosen, KC Worley, ...
Plant and Animal Genome XXIV, 2016
Learn from the Best
V Bernard, SJ Schultheiss, M Michaut
PLoS computational biology 10 (5), e1003645, 2014
MethylScore, a pipeline for accurate and context-aware identification of differentially methylated regions from population-scale plant WGBS data
P Hüther, J Hagmann, A Nunn, I Kakoulidou, R Pisupati, D Langenberger, ...
bioRxiv, 2022
Crowd-sourcing design: Sketch minimization using crowds for feedback
D Engel, V Kottler, C Malisi, M Roettig, EM Willing, S Schultheiss
Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012
Optimizing minimal sketches of visual object categories
D Engel, VA Kottler, CU Malisi, M Röttig, SJ Schultheiss, EM Willing, ...
Pion Ltd., 2010
The power of next-generation sequencing and machine learning for causal gene finding and prediction of phenotypes.
AS Sowa, L Dussling, J Hagmann, SJ Schultheiss
Mutation breeding, genetic diversity and crop adaptation to climate change …, 2021
Higher-Order Machine Learning Models Act As an Approximation of Biological Regulatory Mechanisms
SJ Schultheiss
ASA, CSSA, SSSA International Annual Meeting, 2021
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Articles 1–20