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Min-Redundancy and Max-Relevance Multi-view Feature Selection for Predicting Ovarian Cancer Survival using Multi-omics Data
View ORCID ProfileYasser EL-Manzalawy, Tsung-Yu Hsieh, Manu Shivakumar, Dokyoon Kim, Vasant Honavar
doi: https://doi.org/10.1101/317982
Yasser EL-Manzalawy
1College of Information Sciences and Technology, Pennsylvania State University, University Park, PA 16802
Tsung-Yu Hsieh
1College of Information Sciences and Technology, Pennsylvania State University, University Park, PA 16802
Manu Shivakumar
2Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, Pennsylvania, USA
Dokyoon Kim
2Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, Pennsylvania, USA
3The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania, USA
Vasant Honavar
1College of Information Sciences and Technology, Pennsylvania State University, University Park, PA 16802
3The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania, USA
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Posted May 09, 2018.
Min-Redundancy and Max-Relevance Multi-view Feature Selection for Predicting Ovarian Cancer Survival using Multi-omics Data
Yasser EL-Manzalawy, Tsung-Yu Hsieh, Manu Shivakumar, Dokyoon Kim, Vasant Honavar
bioRxiv 317982; doi: https://doi.org/10.1101/317982
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