4 citations · 9 across the 5 of their papers we have counts for
8 papers
DRIVE: Machine Learning to Identify Drivers of Cancer with High-Dimensional Genomic Data & Imputed Labels
Adnan Akbar, Andrey Solovyev, John W Cassidy +2
Identifying the mutations that drive cancer growth is key in clinical decision making and precision oncology. As driver mutations confer selective advantage and thus have an increa…
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…