4 citations · 9 across the 5 of their papers we have counts for
4 papers · 1 filter
Deep Bayesian Recurrent Neural Networks for Somatic Variant Calling in Cancer
Geoffroy Dubourg-Felonneau, Omar Darwish, Christopher Parsons +4
The emerging field of precision oncology relies on the accurate pinpointing of alterations in the molecular profile of a tumor to provide personalized targeted treatments. Current…
Safety and Robustness in Decision Making: Deep Bayesian Recurrent Neural Networks for Somatic Variant Calling in Cancer
Geoffroy Dubourg-Felonneau, Omar Darwish, Christopher Parsons +4
The genomic profile underlying an individual tumor can be highly informative in the creation of a personalized cancer treatment strategy for a given patient; a practice known as pr…
Learning Embeddings from Cancer Mutation Sets for Classification Tasks
Geoffroy Dubourg-Felonneau, Yasmeen Kussad, Dominic Kirkham +3
Analysis of somatic mutation profiles from cancer patients is essential in the development of cancer research. However, the low frequency of most mutations and the varying rates of…
A Framework for Implementing Machine Learning on Omics Data
Geoffroy Dubourg-Felonneau, Timothy Cannings, Fergal Cotter +4
The potential benefits of applying machine learning methods to -omics data are becoming increasingly apparent, especially in clinical settings. However, the unique characteristics…