A computational pipeline to predict synthetic lethality in cancers from multi-omic cancer data from The Cancer Genome Atlas (TCGA). Features derived from mutation, copy number alteration and gene expression data are combined to develop a multi-parametric Random Forest classifier for prediction of synthetic lethal interaction in cancers.
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A computational pipeline to predict synthetic lethality in cancers from multi-omic cancer data from The Cancer Genome Atlas (TCGA). Features derived from mutation, copy number alteration and gene expression data are combined to develop a multi-parametric Random Forest classifier for prediction of synthetic lethal interaction in cancers.
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ROM2025-siber/DiscoverSL
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A computational pipeline to predict synthetic lethality in cancers from multi-omic cancer data from The Cancer Genome Atlas (TCGA). Features derived from mutation, copy number alteration and gene expression data are combined to develop a multi-parametric Random Forest classifier for prediction of synthetic lethal interaction in cancers.
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