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Advancing Evolutionary Biology: Genomics, Bayesian Statistics, and Machine Learning


Please use this identifier to cite or link to this item: http://hdl.handle.net/2077/66848

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Title: Advancing Evolutionary Biology: Genomics, Bayesian Statistics, and Machine Learning
Authors: Andermann, Tobias
E-mail: tobias.andermann@bioenv.gu.se
Issue Date: 20-Nov-2020
University: University of Gothenburg. Faculty of Science
Institution: Department of Biological and Environmental Sciences ; Institutionen för biologi och miljövetenskap
Parts of work: Andermann, Tobias, Alexandre M. Fernandes, Urban Olsson, Mats Töpel, Bernard Pfeil, Bengt Oxelman, Alexandre Aleixo, Brant C. Faircloth, and Alexandre Antonelli. 2019. “Allele Phasing Greatly Improves the Phylogenetic Utility of Ultraconserved Elements.” Systematic Biology 68 (1): 32–46.
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Andermann, Tobias, Ángela Cano, Alexander Zizka, Christine D. Bacon, and Alexandre Antonelli. 2018. “SECAPR—a Bioinformatics Pipeline for the Rapid and User-Friendly Processing of Targeted Enriched Illumina Sequences, from Raw Reads to Alignments.” PeerJ 6 (July): e5175.
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Andermann, Tobias, Maria Fernanda Torres Jiménez, Pável Matos- Maraví, Romina Batista, José L. Blanco-Pastor, A. Lovisa S. Gustafsson, Logan Kistler, Isabel M. Liberal, Bengt Oxelman, Christine D. Bacon, and Alexandre Antonelli. 2020. “A Guide to Carrying Out a Phylogenomic Target Sequence Capture Project.” Frontiers in Genetics 10.
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Andermann, Tobias, Søren Faurby, Robert Cooke, Daniele Silvestro, and Alexandre Antonelli. 2020. “iucn_sim: A New Program to Simulate Future Extinctions Based on IUCN Threat Status.” Ecography (in print).
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Andermann, Tobias, Søren Faurby, Samuel T. Turvey, Alexandre Antonelli, and Daniele Silvestro. 2020. “The Past and Future Human Impact on Mammalian Diversity.” Science Advances 6 (36): eabb2313.
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Silvestro, Daniele, and Tobias Andermann. 2020. “Prior Choice Affects Ability of Bayesian Neural Networks to Identify Unknowns.” ArXiv Preprint arXiv:2005.04987. http://arxiv.org/abs/2005.04987.
Date of Defence: 2020-12-18
Disputation: Fredagen den 18 december 2020, kl. 14.00, Hörsalen, Botanhuset, Institutionen för Biologi och Miljövetenskap, Carl Skottsbergs gata 22B, Göteborg
Degree: Doctor of Philosophy
Publication type: Doctoral thesis
Keywords: computational biology
bioinformatics
phylogenetics
neural networks
NGS
target capture
Illumina sequencing
fossils
IUCN conservation status
extinction rates
Abstract: During the recent decades the field of evolutionary biology has entered the era of big data, which has transformed the field into an increasingly computational discipline. In this thesis I present novel computational method developments, including their application in empirical case studies. The presented chapters are divided into three fields of computational biology: genomics, Bayesian statistics, and machine learning. While these are not mutually exclusive categories, they do represent differ... more
ISBN: 978-91-8009-136-7
978-91-8009-137-4
URI: http://hdl.handle.net/2077/66848
Appears in Collections:Doctoral Theses from University of Gothenburg / Doktorsavhandlingar från Göteborgs universitet
Doctoral Theses / Doktorsavhandlingar Institutionen för biologi och miljövetenskap

 

 

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