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SigLASSO: a LASSO approach jointly optimizing sampling likelihood and cancer mutation signatures

View ORCID ProfileShantao Li, View ORCID ProfileForrest W. Crawford, View ORCID ProfileMark B. Gerstein
doi: https://doi.org/10.1101/366740
Shantao Li
1Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
2Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA.
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Forrest W. Crawford
3Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
4Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA.
5Yale School of Management, New Haven, Connecticut, USA.
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Mark B. Gerstein
1Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
2Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA.
6Department of Computer Science, Yale University, New Haven, Connecticut, USA.
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Posted July 10, 2018.
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SigLASSO: a LASSO approach jointly optimizing sampling likelihood and cancer mutation signatures
Shantao Li, Forrest W. Crawford, Mark B. Gerstein
bioRxiv 366740; doi: https://doi.org/10.1101/366740
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SigLASSO: a LASSO approach jointly optimizing sampling likelihood and cancer mutation signatures
Shantao Li, Forrest W. Crawford, Mark B. Gerstein
bioRxiv 366740; doi: https://doi.org/10.1101/366740

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