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A general framework for predicting the transcriptomic consequences of non-coding variation
Moustafa Abdalla, Mohamed Abdalla, Mark I. McCarthy, Chris C. Holmes
doi: https://doi.org/10.1101/279323
Moustafa Abdalla
1Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, United Kingdom
2Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, United Kingdom
3Computational Statistics and Machine Learning, Department of Statistics, University of Oxford, United Kingdom
Mohamed Abdalla
4Department of Computer Science, University of Toronto, Canada
Mark I. McCarthy
1Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, United Kingdom
2Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, United Kingdom
5Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, United Kingdom
Chris C. Holmes
1Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, United Kingdom
3Computational Statistics and Machine Learning, Department of Statistics, University of Oxford, United Kingdom
6Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, United Kingdom
Posted March 10, 2018.
A general framework for predicting the transcriptomic consequences of non-coding variation
Moustafa Abdalla, Mohamed Abdalla, Mark I. McCarthy, Chris C. Holmes
bioRxiv 279323; doi: https://doi.org/10.1101/279323
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