Colloquium Mathematics/ Prof. Dr. G. Lunter
|When:||Tu 22-05-2018 16:00|
|Where:||5161.0293 (Zernike, Bernoulliborg)|
Bayesian modeling in biological data analysis:
applications to recombination and human demography
Next-generation sequencing (NGS) has transformed the biological sciences, giving access to genomes, but also to myriad phenotypes including gene expression and DNA-protein interactions. The amount of data generated by NGS pose significant computational and modeling challenges. Perhaps surprisingly, Bayesian approaches remain relevant: signals can be subtle despite the volume of data, and modern Deep Learning models have many free parameters, requiring regularization to prevent overfitting.
I will show applications of Bayesian inference in two modeling problems involving genome-wide data sets. In the first we use particle filters and Variational Bayes to study human demography. Results indicate that the out-of-Africa event might be more complicated than thought, and suggest a new explanation for the relatively high ancient diversity in the African population.
In the second application we adapt a Variational Bayes techniques for Artificial Neural Networks to exploit a symmetry in DNA sequence models. Using low-resolution empirical recombination maps, we infer the binding motifs of PRDM9, a key protein regulating recombination, and obtain a predictive model that matches that of direct experimental measurement of PRDM9 activity. I will show first results of applying these techniques to broader phenotypic prediction from DNA.
Colloquium coordinators are Prof.dr. A.J. van der Schaft ( a.j.van.der.schaft rug.nl ),
Dr. A.V. Kiselev (e-mail: a.v.kiselev rug.nl )