Sparse Network-regularized Nonnegative Matrix Factorization and Applications to Tumor Subtyping
Cancers are complex diseases and identification of clinically important subtypes has the potential to guide better prognosis and treatment. The utility of graph-regularized nonnegative matrix factorization (GNMF) has been demonstrated on tumor subtype identification based on exome-level mutation dat...
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| Format: | Others |
| Language: | en |
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VANDERBILT
2015
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| Online Access: | http://etd.library.vanderbilt.edu/available/etd-07172015-094412/ |