Using Randomised Vectors in Transcription Factor Binding Site Predictions

Finding the location of binding sites in DNA is a difficult problem. Although the location of some binding sites have been experimentally identified, other parts of the genome may or may not contain binding sites. This poses problems with negative data in a trainable classifier. Here we show that using randomized negative data gives a large boost in classifier performance when compared to the original labeled data. Keywords- Genes; Binding Site; Classification; Support Vector Machines

• Type: Journal
• Year: 2010
• Where: ICMLA
• Authors: Faisal Rezwan, Yi Sun, Neil Davey, Rod Adams, Alistair G. Rust, Mark Robinson
• Keywords: Binding Site, ICMLA 2010, Machine Learning, Negative Data, Original Labeled Data