@inproceedings{2df2e5d0d06c4d7d92fd72304bed704c,
title = "Using randomised vectors in transcription factor binding site predictions",
abstract = "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 = "binding site, classification, genes, support vector machines",
author = "F. Rezwan and Yi. Sun and N. Davey and R.G. Adams and A.G. Rust and M. Robinson",
note = "“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.{"} “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.” ",
year = "2010",
doi = "10.1109/ICMLA.2010.82",
language = "English",
isbn = "978-1-4244-9211-4",
pages = "523--527",
booktitle = "Procs of 9th Int Conference on Machine Learning and Applications, ICMLA",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
address = "United States",
}