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The Free Lunch is not over yet—systematic exploration of numerical thresholds in maximum likelihood phylogenetic inference

Abstract (Expand)

Abstract Summary Maximum likelihood (ML) is a widely used phylogenetic inference method. ML implementations heavily rely on numerical optimization routines that use internal numerical thresholds to …tion routines that use internal numerical thresholds to determine convergence. We systematically analyze the impact of these threshold settings on the log-likelihood and runtimes for ML tree inferences with RAxML-NG, IQ-TREE, and FastTree on empirical datasets. We provide empirical evidence that we can substantially accelerate tree inferences with RAxML-NG and IQ-TREE by changing the default values of two such numerical thresholds. At the same time, altering these settings does not significantly impact the quality of the inferred trees. We further show that increasing both thresholds accelerates the RAxML-NG bootstrap without influencing the resulting support values. For RAxML-NG, increasing the likelihood thresholds ϵLnL and ϵbrlen to 10 and 103, respectively, results in an average tree inference speedup of 1.9 ± 0.6 on Data collection 1, 1.8 ± 1.1 on Data collection 2, and 1.9 ± 0.8 on Data collection 2 for the RAxML-NG bootstrap compared to the runtime under the current default setting. Increasing the likelihood threshold ϵLnL to 10 in IQ-TREE results in an average tree inference speedup of 1.3 ± 0.4 on Data collection 1 and 1.3 ± 0.9 on Data collection 2. Availability and implementation All MSAs we used for our analyses, as well as all results, are available for download at https://cme.h-its.org/exelixis/material/freeLunch_data.tar.gz. Our data generation scripts are available at https://github.com/tschuelia/ml-numerical-analysis.

Authors: Julia Haag, Lukas Hübner, Alexey M Kozlov, Alexandros Stamatakis

Date Published: 2023

Publication Type: Journal

Identification of the 18 World War II Executed Citizens of Adele, Rethymnon, Crete Using an Ancient DNA Approach and Low Coverage Genomes

Abstract

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Authors: Nikolaos Psonis, Despoina Vassou, Argyro Nafplioti, Eugenia Tabakaki, Pavlos Pavlidis, Alexandros Stamatakis, Nikos Poulakakis

Date Published: 2023

Publication Type: Journal

3D simulations of strongly magnetized non-rotating supernovae: explosion dynamics and remnant properties

Abstract

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Authors: Vishnu Varma, Bernhard Müller, Fabian R. N. Schneider

Date Published: 2023

Publication Type: Journal

Resource-aware Research on Universe and Matter: Call-to-Action in Digital Transformation

Abstract

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Authors: Ben Bruers, Marilyn Cruces, Markus Demleitner, Guenter Duckeck, Michael Düren, Niclas Eich, Torsten Enßlin, Johannes Erdmann, Martin Erdmann, Peter Fackeldey, Christian Felder, Benjamin Fischer, Stefan Fröse, Stefan Funk, Martin Gasthuber, Andrew Grimshaw, Daniela Hadasch, Moritz Hannemann, Alexander Kappes, Raphael Kleinemühl, Oleksiy M. Kozlov, Thomas Kuhr, Michael Lupberger, Simon Neuhaus, Pardis Niknejadi, Judith Reindl, Daniel Schindler, Astrid Schneidewind, Frank Schreiber, Markus Schumacher, Kilian Schwarz, Achim Streit, R. Florian von Cube, Rod Walker, Cyrus Walther, Sebastian Wozniewski, Kai Zhou

Date Published: 2023

Publication Type: Journal

Enhanced sampling simulations and machine learning to address infectious diseases

Abstract

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Author: Matheus Ferraz

Date Published: 2023

Publication Type: Doctoral Thesis

Shot noise reduction in radiographic and tomographic multi-channel imaging with self-supervised deep learning

Abstract (Expand)

Shot noise is a critical issue in radiographic and tomographic imaging, especially when additional constraints lead to a significant reduction of the signal-to-noise ratio. This paper presents a method …. This paper presents a method for improving the quality of noisy multi-channel imaging datasets, such as data from time or energy-resolved imaging, by exploiting structural similarities between channels. To achieve that, we broaden the application domain of the Noise2Noise self-supervised denoising approach. The method draws pairs of samples from a data distribution with identical signals but uncorrelated noise. It is applicable to multi-channel datasets if adjacent channels provide images with similar enough information but independent noise. We demonstrate the applicability and performance of the method via three case studies, namely spectroscopic X-ray tomography, energy-dispersive neutron tomography, and in vivo X-ray cine-radiography.

Authors: Yaroslav Zharov, Evelina Ametova, Rebecca Spiecker, Tilo Baumbach, Genoveva Burca, Vincent Heuveline

Date Published: 2023

Publication Type: Journal

Regression diagnostics meets forecast evaluation: Conditional calibration, reliability diagrams, and coefficient of determination

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Authors: Tilmann Gneiting, Johannes Resin

Date Published: 2023

Publication Type: Journal

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