Using sampling methods to improve binding site predictions

Currently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. In previous work we combine random selection under-sampling with the SMOTE over-sampling techniques, working with several classification algorithms from the machine learning field to integrate binding site predictions. In this paper, we improve the classification result with the aid of Tomek links, either as an under-sampling technique or to remove further noisy data after sampling.

• Type: Conference
• Year: 2006
• Where: ESANN
• Authors: Yi Sun, Mark Robinson, Rod Adams, Rene te Boekhorst, Alistair G. Rust, Neil Davey
• Keywords: Binding Site Predictions, Classification Algorithms, ESANN 2006, ESANN 2007, Factor Binding Site