Publications

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1702 Publications visible to you, out of a total of 1702

Abstract

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Authors: A.N. Vantyghem, T.J. Galvin, B. Sebastian, C.P. O’Dea, Y.A. Gordon, M. Boyce, L. Rudnick, K. Polsterer, H. Andernach, M. Dionyssiou, P. Venkataraman, R. Norris, S.A. Baum, X.R. Wang, M. Huynh

Date Published: 1st Apr 2024

Publication Type: Journal

Abstract

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Authors: Leif Seute, Eric Hartmann, Jan Stühmer, Frauke Gräter

Date Published: 25th Mar 2024

Publication Type: InProceedings

Abstract (Expand)

In traditional studies on language evolution, scholars often emphasize the importance of sound laws and sound correspondences for phylogenetic inference of language family trees. However, to date, computational approaches have typically not taken this potential into account. Most computational studies still rely on lexical cognates as major data source for phylogenetic reconstruction in linguistics, although there do exist a few studies in which authors praise the benefits of comparing words at the level of sound sequences. Building on (a) ten diverse datasets from different language families, and (b) state-of-the-art methods for automated cognate and sound correspondence detection, we test, for the first time, the performance of sound-based versus cognate-based approaches to phylogenetic reconstruction. Our results show that phylogenies reconstructed from lexical cognates are topologically closer, by approximately one third with respect to the generalized quartet distance on average, to the gold standard phylogenies than phylogenies reconstructed from sound correspondences.

Authors: Luise Häuser, Gerhard Jäger, Johann-Mattis List, Taraka Rama, Alexandros Stamatakis

Date Published: 22nd Mar 2024

Publication Type: Proceedings

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Editor:

Date Published: 18th Mar 2024

Publication Type: Master's Thesis

Abstract

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Authors: Xianghe Ma, Michael Strube, Wei Zhao

Date Published: 17th Mar 2024

Publication Type: InProceedings

Abstract (Expand)

Motivation: Genotype datasets typically contain a large number of single nucleotide polymorphisms for a comparatively small number of individuals. To identify similarities between individuals and to infer an individual’s origin or membership to a cultural group, dimensionality reduction techniques are routinely deployed. However, inherent (technical) difficulties such as missing or noisy data need to be accounted for when analyzing a lower dimensional representation of genotype data, and the uncertainty of such an analysis should be reported in all studies. However, to date, there exists no stability estimation technique for genotype data that can estimate this uncertainty. Results: Here, we present Pandora, a stability estimation framework for genotype data based on bootstrapping. Pandora computes an overall score to quantify the stability of the entire embedding, perindividual support values, and deploys a k-means clustering approach to assess the uncertainty of assignments to potential cultural groups. In addition to this bootstrap-based stability estimation, Pandora offers a sliding-window stability estimation for whole-genome data. Using published empirical and simulated datasets, we demonstrate the usage and utility of Pandora for studies that rely on dimensionality reduction techniques. Data and Code: Availability Pandora is available on GitHub https://github.com/tschuelia/Pandora. All Python scripts and data to reproduce our results are available on GitHub https://github.com/tschuelia/PandoraPaper.

Authors: Julia Haag, Alexander I. Jordan, Alexandros Stamatakis

Date Published: 15th Mar 2024

Publication Type: Journal

Abstract

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Authors: Jack Fosten, Daniel Gutknecht, Marc-Oliver Pohle

Date Published: 12th Mar 2024

Publication Type: Journal

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