Author

S. Burgstaller-Muehlbacher

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Biology Disciplines

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Evolutionary Biology· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:phylogenetics / evolution· 2 Assay:DNA methylation· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

A. von Haeseler· 2 shared H. A. Schmidt· 2 shared S. M. Crotty· 2 shared T. Drucks· 2 shared A. F. Leuchtenberger· 1 shared

Activity by Year

2019-2021 · peak 1/yr

Co-Author Collaboration Network

5 top collaborators · 10 mutual co-author links

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2 shared papersEvolutionary Biology

A. von Haeseler

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2 papers

Recent·Most cited

Evolutionary BiologybioRxivDec 22, 2021

ModelRevelator: Fast phylogenetic model estimation via deep learning

S. Burgstaller-Muehlbacher, S. M. Crotty, H. A. Schmidt…·A. von Haeseler

ModelRevelator uses deep learning to select phylogenetic models from multiple-sequence-alignments, avoiding computationally expensive likelihood-based methods.

DOI 10.1016/j.ympev.2023.107905

DNA methylationwhole-genome sequencing

𝕏 PostBluesky

Evolutionary BiologybioRxivOct 29, 2019

Distinguishing Felsenstein zone from Farris zone using neural networks

T. Drucks, A. F. Leuchtenberger, S. Burgstaller-Muehlbacher…·A. von Haeseler

This paper uses neural networks to distinguish between long-branch attraction and repulsion in four-taxon alignments, improving tree reconstruction accuracy by selecting optimal methods.

DOI 10.1093/molbev/msaa164

deep learningphylogenetics / evolution

𝕏 PostBluesky