Using Feature Selection Filtering Methods for Binding Site Predictions

摘要：

Currently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. In previous work we applied classification techniques on predictions from 12 key prediction algorithms. In this paper,we investigate the classification results when 4 feature selection filtering methods are used. They are Bi-Normal Separation, correlation coefficients, F-Score and a cross entropy based algorithm. It is found that all 4 filtering methods perform equally well. Moreover, we show that the worst performing algorithms are not detrimental to the overall performance.

关键词： Feature Selection Filters Support Vector Machines Transcription Factors.

作者: Yi Sun Mark Robinson Rod Adams Rene te Boekhorst Alistair G. Rust Neil Davey

作者单位: Science and Technology Research Institute University of Hertfordshire, UK

会议类型: 国际会议

会议名称: Firth IEEE International Conference on Cognitive Informatics(第五届认知信息国际会议)

会议地点: 北京

会议语种:英文

页码: 566-571

在线出版日期: 2006-07-17（万方平台首次上网日期，不代表论文的发表时间）