As the cost of genomic technologies continues to decrease and large scale profiling of molecular features in diseases and their changing expression after exposure to drugs becomes more readily available, leveraging molecular data for drug discovery becomes increasingly more important. Existing studies largely rely on the belief that drugs which reverse the expression of disease associated genes have potential to be efficacious for treating the disease in question, and thus statistics that can effectively summarize this reversal relationship are in high demand. We propose a rank based count statistic for detecting such reversal relationships. This statistic is robust to outliers and we have derived results concerning its asymptotic behavior. We also propose a gene-level statistic for detecting potential drug-target genes. In simulation studies and real data we see that our statistics are comparable to or outperform other measures in these scenarios.

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