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SVFX: a machine-learning framework to quantify the pathogenicity of structural variants
View ORCID ProfileSushant Kumar, View ORCID ProfileArif Harmanci, Jagath Vytheeswaran, View ORCID ProfileMark B. Gerstein
doi: https://doi.org/10.1101/739474
Sushant Kumar
1Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
2Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
Arif Harmanci
3Center for Precision Health, School of Biomedical Informatics, University of Texas Health Sciences Center, Houston, Texas, 77030, USA
Jagath Vytheeswaran
4Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, 91125, USA
Mark B. Gerstein
1Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
2Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
5Department of Computer Science, Yale University, 260/266 Whitney Avenue PO Box 208114, New Haven, CT 06520, USA
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Posted August 19, 2019.
SVFX: a machine-learning framework to quantify the pathogenicity of structural variants
Sushant Kumar, Arif Harmanci, Jagath Vytheeswaran, Mark B. Gerstein
bioRxiv 739474; doi: https://doi.org/10.1101/739474
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