Florian Schmidt
Florian Schmidt
Genome Institute of Singapore, A*STAR
Verified email at mmci.uni-saarland.de
Cited by
Cited by
The International Human Epigenome Consortium: a blueprint for scientific collaboration and discovery
HG Stunnenberg, S Abrignani, D Adams, M de Almeida, L Altucci, V Amin, ...
Cell 167 (5), 1145-1149, 2016
Epigenomic profiling of human CD4+ T cells supports a linear differentiation model and highlights molecular regulators of memory development
P Durek, K Nordström, G Gasparoni, A Salhab, C Kressler, M De Almeida, ...
Immunity 45 (5), 1148-1161, 2016
Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction
F Schmidt, N Gasparoni, G Gasparoni, K Gianmoena, C Cadenas, ...
Nucleic acids research 45 (1), 54-66, 2017
RegulatorTrail: a web service for the identification of key transcriptional regulators
T Kehl, L Schneider, F Schmidt, D Stöckel, N Gerstner, C Backes, ...
Nucleic acids research 45 (W1), W146-W153, 2017
TEPIC 2—an extended framework for transcription factor binding prediction and integrative epigenomic analysis
F Schmidt, F Kern, P Ebert, N Baumgarten, MH Schulz
Bioinformatics 35 (9), 1608-1609, 2019
Unique and assay specific features of NOMe-, ATAC-and DNase I-seq data
KJV Nordström, F Schmidt, N Gasparoni, A Salhab, G Gasparoni, K Kattler, ...
Nucleic acids research 47 (20), 10580-10596, 2019
Integrative prediction of gene expression with chromatin accessibility and conformation data
F Schmidt, F Kern, MH Schulz
Epigenetics & chromatin 13 (1), 4, 2020
On the problem of confounders in modeling gene expression
F Schmidt, MH Schulz
Bioinformatics 35 (4), 711-719, 2019
An ontology-based method for assessing batch effect adjustment approaches in heterogeneous datasets
F Schmidt, M List, E Cukuroglu, S Köhler, J Göke, MH Schulz
Bioinformatics 34 (17), i908-i916, 2018
EpiRegio: analysis and retrieval of regulatory elements linked to genes
N Baumgarten, D Hecker, S Karunanithi, F Schmidt, M List, MH Schulz
Nucleic acids research 48 (W1), W193-W199, 2020
Temporal enhancer profiling of parallel lineages identifies AHR and GLIS1 as regulators of mesenchymal multipotency
D Gérard, F Schmidt, A Ginolhac, M Schmitz, R Halder, P Ebert, ...
Nucleic acids research 47 (3), 1141-1163, 2019
Integrative analysis of epigenetics data identifies gene-specific regulatory elements
F Schmidt, A Marx, M Hebel, M Wegner, N Baumgarten, M Kaulich, ...
bioRxiv, 585125, 2019
Temporal epigenomic profiling identifies AHR and GLIS1 as super-enhancer controlled regulators of mesenchymal multipotency
D Gérard, F Schmidt, A Ginolhac, M Schmitz, R Halder, P Ebert, ...
BioRxiv, 183988, 2018
Prediction of single-cell gene expression for transcription factor analysis
F Behjati Ardakani, K Kattler, T Heinen, F Schmidt, D Feuerborn, ...
GigaScience 9 (11), giaa113, 2020
Machine learning for deciphering cell heterogeneity and gene regulation
M Scherer, F Schmidt, O Lazareva, J Walter, J Baumbach, MH Schulz, ...
Nature Computational Science 1 (3), 183-191, 2021
DUBStepR: correlation-based feature selection for clustering single-cell RNA sequencing data
B Ranjan, W Sun, J Park, R Xie, F Alipour, V Singhal, S Prabhakar
bioRxiv, 2020
Predicting transcription factor binding using ensemble random forest models
FB Ardakani, F Schmidt, MH Schulz
F1000Research 7, 2018
CausalTrail: Testing hypothesis using causal Bayesian networks
D Stöckel, F Schmidt, P Trampert, HP Lenhof
F1000Research 4, 2015
scConsensus: combining supervised and unsupervised clustering for cell type identification in single-cell RNA sequencing data
B Ranjan, F Schmidt, W Sun, J Park, MA Honardoost, J Tan, NA Rayan, ...
BMC bioinformatics 22 (1), 1-15, 2021
Computational prediction of CRISPR-impaired non-coding regulatory regions
N Baumgarten, F Schmidt, M Wegner, M Hebel, M Kaulich, MH Schulz
Biological Chemistry, 2021
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