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Activity Number: 491
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #319325 View Presentation
Title: Stochastic Network Models with Applications to 'Omics Data
Author(s): Thomas Bartlett*
Companies: University College London
Keywords: Stochastic networks ; genomics ; genetics ; computational statistics
Abstract:

Networks and other non-Euclidean relational datasets have become important applications in modern statistics. Network models have been shown to be extraordinarily powerful for representing and analysing very large, high-dimensional data-sets. Complex systems which can be modelled as networks are ubiquitous, and the methodology which has resulted has been found to be particularly useful in the study of cell biology. However, cell biological processes are inherently stochastic and non-stationary, and empirical networks based on high-throughput assays suffer from much technical noise. I will present statistical and computational network models with applications relevant to the study of developmental and disease processes, such as cancer. I will propose models for inference of uni- and bi-partitite network structure, and I will present preliminary results from recent work to develop these models such that they take account of dynamic network structure.

https://www.ucl.ac.uk/statistics/people/thomas-bartlett


Authors who are presenting talks have a * after their name.

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