Dominik Schrempf
Dominik Schrempf
Dept. Biological Physics, Eötvös University, Pázmány P. stny. 1A., H-1117 Budapest, Hungary
Verified email at caesar.elte.hu - Homepage
Title
Cited by
Cited by
Year
IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era
BQ Minh, HA Schmidt, O Chernomor, D Schrempf, MD Woodhams, ...
Molecular biology and evolution 37 (5), 1530-1534, 2020
9472020
The comparative genomics and complex population history of Papio baboons
J Rogers, M Raveendran, RA Harris, T Mailund, K Leppälä, ...
Science Advances 5 (1), eaau6947, 2019
792019
Temperature control of ion guiding through insulating capillaries
E Gruber, G Kowarik, F Ladinig, JP Waclawek, D Schrempf, F Aumayr, ...
Physical Review A 86 (6), 062901, 2012
602012
PoMo: an allele frequency-based approach for species tree estimation
N De Maio, D Schrempf, C Kosiol
Systematic biology 64 (6), 1018-1031, 2015
562015
Reversible polymorphism-aware phylogenetic models and their application to tree inference
D Schrempf, BQ Minh, N De Maio, A von Haeseler, C Kosiol
Journal of theoretical biology 407, 362-370, 2016
482016
An alternative derivation of the stationary distribution of the multivariate neutral Wright–Fisher model for low mutation rates with a view to mutation rate estimation from …
D Schrempf, A Hobolth
Theoretical population biology 114, 88-94, 2017
182017
Polymorphism-aware species trees with advanced mutation models, bootstrap, and rate heterogeneity
D Schrempf, BQ Minh, A von Haeseler, C Kosiol
Molecular biology and evolution 36 (6), 1294-1301, 2019
102019
Scalable empirical mixture models that account for across-site compositional heterogeneity
D Schrempf, N Lartillot, G Szöllősi
Molecular biology and evolution 37 (12), 3616-3631, 2020
82020
IQ-TREE version 2.1. 2: Tutorials and Manual Phylogenomic software by maximum likelihood
BQ Minh, R Lanfear, J Trifinopoulos, D Schrempf, HA Schmidt
72021
K. T okési, P. Gunacker, T. Schweigler, C. Lemell, J. Burgdörfer
E Gruber, G Kowarik, F Ladinig, JP Waclawek, D Schrempf, F Aumayr, ...
Phys. Rev. A 86, 062901, 2012
52012
The sources of phylogenetic conflicts
D Schrempf, G Szöllősi
Phylogenetics in the Genomic Era, 3.1: 1--3.1: 23, 2020
42020
Inference in population genetics using forward and backward, discrete and continuous time processes
J Bergman, D Schrempf, C Kosiol, C Vogl
Journal of theoretical biology 439, 166-180, 2018
42018
The effect of temperature on guiding of slow highly charged ions through a mesoscopic glass capillary
RJ Bereczky, G Kowarik, F Ladinig, D Schrempf, K Tökési, F Aumayr
Journal of Physics: Conference Series 388 (13), 132031, 2012
32012
Inferring the deep past from molecular data
TA Williams, D Schrempf, GJ Szöllősi, CJ Cox, PG Foster, TM Embley
Genome Biology and Evolution 13 (5), evab067, 2021
12021
Relative time constraints improve molecular dating
GJ Szollosi, S Höhna, TA Williams, D Schrempf, V Daubin, B Boussau
bioRxiv, 2020.10. 17.343889, 2021
12021
An ultra-compact setup for measuring ion-induced electron emission statistics
D Schrempf, W Meissl, F Aumayr
Nuclear Instruments and Methods in Physics Research Section B: Beam …, 2013
12013
Development of an ultra-compact setup for measuring ion-induced electron emission statistics
D Schrempf
12013
Distinguishing excess mutations and increased cell death based on variant allele frequencies
G Tibély, D Schrempf, I Derényi, GJ Szöllősi
bioRxiv, 2021.02. 12.430830, 2021
2021
Simultaneous estimation of per cell division mutation rate and turnover rate from bulk tumour sequence data
G Tibély, D Schrempf, I Derényi, GJ Szöllősi
bioRxiv, 2021
2021
Scalable empirical mixture models that account
D Schrempf, N Lartillot, G Szöllősi
2019
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Articles 1–20