Computational transcription factor binding prediction using random forests

Gene regulation in eukaryotes is a very complicated and myriad procedure. It is a diverse action which include finding the protein coding regions, locating transcription factor binding sites, promoter identification and determination of cis and transregulatory elements. Transcription factor binding prediction is very costly using experimental techniques. So computational methods can be used for prediction and the predicted results can be experimentally validated. A genome can be selected for prediction, structural and sequential features can be selected and Principal Component Analysis can be done which show the most relevant features. A random forest classifier can be used for the prediction classification and results can be evaluated for performance assessment.