Publications

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1703 Publications visible to you, out of a total of 1703

Abstract

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Authors: Chloé Braud, Christian Hardmeier, Junyi Jessy Li, Annie Louis, Michael Strube, Amir Zeldes

Date Published: 10th Nov 2021

Publication Type: Proceedings

Abstract

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Authors: Valentina Disarlo, Huiping Pan, Anja Randecker, Robert Tang

Date Published: 1st Nov 2021

Publication Type: Journal

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Authors: Zhi Hu, Pengfei Huang

Date Published: 1st Nov 2021

Publication Type: Journal

Abstract

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Authors: L. Mahy, H. Sana, F. Martins, G. Rauw, M. Abdul-Masih, L. A. Almeida, N. Langer, K. Sen, T. Shenar, A. de Koter, S. E. de Mink, C. J. Evans, A. F. J. Moffat, S. Simón-Dı́az, F. R. N. Schneider, R. Barbá, J. S. Clark, M. Fabry, P. Crowther, G. Gräfener, D. J. Lennon, F. Tramper, J. S. Vink

Date Published: 1st Nov 2021

Publication Type: InProceedings

Abstract (Expand)

Phylogenetic trees represent hypothetical evolutionary relationships between organisms. Approaches for inferring phylogenetic trees include the Maximum Likelihood (ML) method. This method relies on numerical optimization routines that use internal numerical thresholds. We analyze the influence of these thresholds on the likelihood scores and runtimes of tree inferences for the ML inference tools RAxML-NG, IQ-Tree, and FastTree. We analyze 22 empirical datasets and show that we can speed up the tree inference in RAxML-NG and IQ-Tree by changing the default values of two such numerical thresholds. Using 15 additional simulated datasets, we show that these changes do not affect the accuracy of the inferred phylogenetic trees. For RAxML-NG, increasing the likelihood thresholds lh_epsilon and spr_lh_epsilon to 10 and 103 respectively results in an average speedup of 1.9 ± 0.6. Increasing the likelihood threshold lh_epsilon in IQ-Tree results in an average speedup of 1.3 ± 0.4. In addition to the numerical analysis, we attempt to predict the difficulty of datasets, with the aim of preventing an unnecessarily large number of tree inferences for datasets that are easy to analyze. We present our prediction experiments and discuss why this task proved to be more challenging than anticipated.

Author: Julia Haag

Date Published: 31st Oct 2021

Publication Type: Master's Thesis

Abstract

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Authors: N. Gianniotis, F. Pozo Nuñez, K. L. Polsterer

Date Published: 29th Oct 2021

Publication Type: Journal

Abstract

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Authors: Friedrich Kallinowski, Yannique Ludwig, Dominik Gutjahr, Christian Gerhard, Hannah Schulte-Hörmann, Lena Krimmel, Carolin Lesch, Katharina Uhr, Philipp Lösel, Samuel Voß, Vincent Heuveline, Matthias Vollmer, Johannes Görich, Regine Nessel

Date Published: 29th Oct 2021

Publication Type: Journal

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