|Datum||22 May 2018, 16.00 - 17.00h|
|Lokatie||BB 5161.0253 (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.
|Organisator||Rijksuniversiteit Groningen (email)|
|Geplaatst door||Heleen de Waard|