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05/02/2021
DRIVE: Machine Learning to Identify Drivers of Cancer with High-Dimensional Genomic Data Imputed Labels
Identifying the mutations that drive cancer growth is key in clinical de...
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12/06/2019
Deep Bayesian Recurrent Neural Networks for Somatic Variant Calling in Cancer
The emerging field of precision oncology relies on the accurate pinpoint...
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12/04/2019
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...
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11/28/2019
Effective Sub-clonal Cancer Representation to Predict Tumor Evolution
The majority of cancer treatments end in failure due to Intra-Tumor Hete...
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11/27/2019
Flatsomatic: A Method for Compression of Somatic Mutation Profiles in Cancer
In this study, we present Flatsomatic - a Variational Auto Encoder (VAE)...
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11/20/2019
Learning Embeddings from Cancer Mutation Sets for Classification Tasks
Analysis of somatic mutation profiles from cancer patients is essential ...
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11/26/2018
Interlacing Personal and Reference Genomes for Machine Learning Disease-Variant Detection
DNA sequencing to identify genetic variants is becoming increasingly val...
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11/26/2018
