Every one knows that most computational predictions on biological systems are "less confidential" although many methods were based on convincible evidences and were announced of having high accuracy. But why these theoretically applicable methods failed to give reliable outputs? Some people believe that it's simply because existing methods are not good enough, or the biological system is too complex to predict. That's probably true. But there's another important factor that was often ignored, the abundance of potential true positives.
I will use a simple disease diagnosis example to show that even if we have a excellent prediction method based on strong evidences, we might still get poor predictions as long as the disease is rare in population.
