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S1: Statistical and Computational approaches in Evolution
Monday 9th June, 9th June 09.30 - 11.00
San Geronimo
O-1 A deep learning approach to ancestral inference
Sara Sheehan*, Yun S. Song
UC Berkeley, USA
O-2 A model-based approach for identifying signatures of ancient balancing selection in genetic data
Michael DeGiorgio*1, Kirk Lohmueller2, Rasmus Nielsen3
1Pennsylvania State University, USA, 2University of California, Los Angeles, USA, 3University of California, Berkeley, USA
O-3 Detecting tracts of introgressed Neanderthal ancestry in genomic sequence data of modern humans
Matthias Steinrücken*2, John A. Kamm1, Emilia Wieczorek3, Yun S. Song1,2
1Dept. of Stat., UC Berkeley, USA, 2EECS Dept., UC Berkeley, USA, 3Dept. of Math., UC Berkeley, USA
O-4 A robust method for detecting and classifying hybrids from microsatellite data, with application to African elephants
Ida Moltke*1, Samrat Mondol2, Samuel K Wasser2, Matthew Stephens1
1University of Chicago, USA, 2University of Washington, USA
O-5 An Adaptive Branch Site Random Effects Model for Efficient Detection of Episodic Diversifying Selection
Martin Smith*, Joel Wertheim, Konrad Scheffler, Ben Murrell, Sergei Kosakovsky Pond
University of California, San Diego, USA
O-6 Application of kernel approximate Bayesian computation to demographic inferences
Shigeki Nakagome*1,2, Shuhei Mano2
1Department of Human Genetics, The University of Chicago, USA, 2The Institute of Statistical Mathematics, Japan