Dear Colleagues,

With colleagues from the School of Computer Sciences at the University
of St Andrews (Juan Ye and Simon Dobson), we would like to recruit a
postdoctoral scientist with strong computational biology background to
work on a project funded by the BBSRC. The focus is on the development
of explainable/interpretable deep learning and Complex Network Analysis
methods to infer the genetic architecture of complex traits.

Details of the project are provided below. The
official advert for the postdoc can be found here:
https://www.vacancies.st-andrews.ac.uk/Vacancies/W/6849/0/456379/889/research-fellow-ar3187

I would greatly appreciate it if you could forward the advert (see
below) to good early career researchers you may know and to also spread
the word among your colleagues. Interested candidates can contact me
(oeg@st-andrews.ac.uk) to find out more about the project.

Thanks in advance. Best wishes,

Oscar

Oscar Gaggiotti
School of Biology
University of St Andrews

Opportunity for postdoctoral fellow to join a BBSRC funded project on
applications of AI and complex network analysis to population genomics
questions

It is now clear that complex phenotypic traits may be determined not only
by many genes of small effect but also by so-called epistatic interactions
among them. Some progress has been made in detecting interactions
among a small number of variants, but the role of high-order epistatic
interactions still needs to be addressed. Thus, the challenge today is
to develop new methods of analysis that can scale up to modern population
genomics databases and uncover interactions between many genetic variants.

Our project addresses these challenges by harnessing the power of deep
learning (DL) methods and complex network analysis (CNA) to develop an
end-to-end computational tool to associate causal genetic variants
to a phenotype of interest and also detect underlying epistatic
interactions. Our approach will go beyond pairwise gene-to-gene
interactions and study higher-order interactions.

We will implement DL models that scale up to high-dimensional input
and learn complex nonlinear interaction patterns, which can then be
unveiled using the latest advances in explainable and interpretable
machine learning approaches. Once important variants and potential
low dimensional interactions are identified, complex network analysis
(CNA) techniques will allow us to explore higher-order interactions
using an enormous range of new analysis methods that are unavailable in
lower-order settings. Our approach will help identify essential genes
(as network hubs), gene clusters with similar functionalities, and genes
with suppressing and augmenting effects for a specific phenotype.

We are looking to recruit a postdoctoral researcher with a strong
background in computational biology. Interested candidates should contact
Oscar Gaggiotti (oeg@st-andrews.ac.uk).

Oscar Gaggiotti <oeg@st-andrews.ac.uk>

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