Using randomised vectors in transcription factor binding site predictions

F. Rezwan; Yi. Sun; N. Davey; R.G. Adams; A.G. Rust; M. Robinson

doi:10.1109/ICMLA.2010.82

Using randomised vectors in transcription factor binding site predictions

F. Rezwan
, Yi. Sun
, N. Davey
, R.G. Adams
, A.G. Rust
, M. Robinson

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

61 Downloads (Pure)

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.

Original language	English
Title of host publication	Procs of 9th Int Conference on Machine Learning and Applications, ICMLA
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	523-527
ISBN (Print)	978-1-4244-9211-4
DOIs	https://doi.org/10.1109/ICMLA.2010.82
Publication status	Published - 2010

Keywords

binding site
classification
genes
support vector machines

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10.1109/ICMLA.2010.82

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“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.”
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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",

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year = "2010",

doi = "10.1109/ICMLA.2010.82",

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AU - Rezwan, F.

AU - Sun, Yi.

AU - Davey, N.

AU - Adams, R.G.

AU - Rust, A.G.

AU - Robinson, M.

N1 - “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.”

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

KW - binding site

KW - classification

KW - genes

KW - support vector machines

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U2 - 10.1109/ICMLA.2010.82

DO - 10.1109/ICMLA.2010.82

M3 - Conference contribution

SN - 978-1-4244-9211-4

SP - 523

EP - 527

BT - Procs of 9th Int Conference on Machine Learning and Applications, ICMLA

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Using randomised vectors in transcription factor binding site predictions

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Keywords

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