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Modeling Transcription Factor Target Promoters 145Fig. 2. Variable importance: relative importance of top ranking variables given byRandom Forest analysis, ranked in the decreasing order of importance with respect tomean decrease in accuracy and Gini measures.genomics approaches have reached a reasonable sophistication in identifyingTFBSs of known PWMs (48). Using combination of different programs and takingthe consensus predictions for considering the reliable predictions is suggested. But,the author expects some amount of noise in terms of false predictions and missedreal TFBS within a given promoter. Further, inclusion of novel TFBS is not consideredin the approaches suggested in this chapter, although one can make PWM andinclude it as a new variable. However, even the partial predictions are of immensevalue to design the experiments that can determine the regulatory modules fasterthan would be possible by experimental methods alone.2. Recent programs, such as rVISTA (57) and ConSite (58), incorporate both sequenceconservation across orthologous promoters and high-quality PWM models in producingmore reliable TFBSs predictions.3. Dimensionality reduction is an important problem in pattern recognition. In mostof the experimental situations, lot more number of features/variables (TFs) areavailable than the number of cases (promoters). Selecting the appropriate numberof features to build the classifier is an important problem, and Random Foresthelps to reduce the dimensionality of feature space for effective classification (65).