Skip to main content


Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Fig. 4 | Human Genomics

Fig. 4

From: Computational analysis of mRNA expression profiling in the inner ear reveals candidate transcription factors associated with proliferation, differentiation, and deafness

Fig. 4

Choosing a threshold probability for discrimination of deafness-associated genes. The two plots demonstrate the effect of choosing a threshold on the balance between sensitivity and specificity of prediction. Top: a standard ROC curve. A gene associated with deafness according to any of the text mining tools is considered a positive gene, all others are considered negative. Bottom: threshold determination based on a comparison of association scores. At each threshold, significance according to the Wilcoxon rank sum test is assigned to the difference between the association scores of genes above the threshold and all other genes. A higher −log2P value indicates a more significant difference, in the direction of higher association scores for genes above the threshold. Line color indicates the source of the association scores used. Each graph has two thresholds marked. The first is the threshold value for which the sum of specificity and sensitivity of the ROC curve is highest (upper, circle shape; lower, solid vertical line). The second is the threshold value for which the Wilcoxon test is most significant for the “Combined” association score (upper, triangle shape; lower, dotted line)

Back to article page