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Deep Bayesian Recurrent Neural Networks for Somatic Variant Calling in Cancer
The emerging field of precision oncology relies on the accurate pinpoint...
12/06/2019 ∙ by Geoffroy Dubourg-Felonneau, et al. ∙
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A Framework for Implementing Machine Learning on Omics Data
The potential benefits of applying machine learning methods to -omics da...
11/26/2018 ∙ by Geoffroy Dubourg-Felonneau, et al. ∙
4
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Interlacing Personal and Reference Genomes for Machine Learning Disease-Variant Detection
DNA sequencing to identify genetic variants is becoming increasingly val...
11/26/2018 ∙ by Luke R Harries, et al. ∙
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Learning Embeddings from Cancer Mutation Sets for Classification Tasks
Analysis of somatic mutation profiles from cancer patients is essential ...
11/20/2019 ∙ by Geoffroy Dubourg-Felonneau, et al. ∙
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Flatsomatic: A Method for Compression of Somatic Mutation Profiles in Cancer
In this study, we present Flatsomatic - a Variational Auto Encoder (VAE)...
11/27/2019 ∙ by Geoffroy Dubourg-Felonneau, et al. ∙
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Effective Sub-clonal Cancer Representation to Predict Tumor Evolution
The majority of cancer treatments end in failure due to Intra-Tumor Hete...
11/28/2019 ∙ by Adnan Akbar, et al. ∙
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Safety and Robustness in Decision Making: Deep Bayesian Recurrent Neural Networks for Somatic Variant Calling in Cancer
The genomic profile underlying an individual tumor can be highly informa...
12/04/2019 ∙ by Geoffroy Dubourg-Felonneau, et al. ∙
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