Hybrid-based Drug Prioritization
This module is based on hybrid machine learning models of mutation/varioations, expression and CNV input features. Here user has to first select the type of hybrid between mutation and variation and then upload or pase the expression and CNV information of whole genome, as shown in the example. All the fields are mandatory for submission.
For more information see HELP page.


Here user has to submit the mRNA expression data on Affymetrix Human Genome U133 Plus 2.0 arrays with background correction done by RMA (Robust Multichip Average) and quantile normalization (as described in CCLE database). The input has to be in two columns where first column has to be Hugo names and second column should be expression values as given in example.

Paste GENE names

Upload Expression Gene File  


Here user just needs to submit the Hugo name list of mutated OR variant genes in whole genome sequencing, as given in example.

Mutation VaritaionSource Genes

Paste GENE names

Upload Mutation/Variation Gene File  


The input file should have CNV of whole genome, in the form of log2 of ratio of copy numbers of cancer vs. normal genes of whole genome as given in the example file.
The submission CNV file needs to have HUGO symbol of gene in first column and CNV values in the second column (as described in CCLE database). .

Paste GENE names

Upload CNV Gene File