4 citations · 9 across the 5 of their papers we have counts for
5 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…
Effective Sub-clonal Cancer Representation to Predict Tumor Evolution
Adnan Akbar, Geoffroy Dubourg-Felonneau, Andrey Solovyev +3
The majority of cancer treatments end in failure due to Intra-Tumor Heterogeneity (ITH). ITH in cancer is represented by clonal evolution where different sub-clones compete with ea…
Flatsomatic: A Method for Compression of Somatic Mutation Profiles in Cancer
Geoffroy Dubourg-Felonneau, Yasmeen Kussad, Dominic Kirkham +3
In this study, we present Flatsomatic - a Variational Auto Encoder (VAE) optimized to compress somatic mutations that allow for unbiased data compression whilst maintaining the sig…
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…