Using randomised vectors in transcription factor binding site predictions

Faisal I Rezwan; Yi Sun; Neil Davey; Rod Adams; Alistair G. Rust; Mark Robinson

doi:10.1109/ICMLA.2010.82

Using randomised vectors in transcription factor binding site predictions

Faisal I Rezwan
, Yi Sun
, Neil Davey
, Rod Adams
, Alistair G. Rust
, Mark Robinson

University of Hertfordshire
Wellcome Genome Campus
Michigan State University

Allbwn ymchwil: Cyfraniad at gynhadledd › Papur › adolygiad gan gymheiriaid

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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.

Iaith wreiddiol	Saesneg
Tudalennau	523-527
Nifer y tudalennau	5
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1109/ICMLA.2010.82
Statws	Cyhoeddwyd - 12 Rhag 2010
Cyhoeddwyd yn allanol	Ie
Digwyddiad	9th IEEE International Conference on Machine Learning and Applications (ICMLA) 2010 - Washington DC, Unol Daleithiau America Hyd: 12 Rhag 2010 → 14 Rhag 2010

Cynhadledd

Cynhadledd	9th IEEE International Conference on Machine Learning and Applications (ICMLA) 2010
Teitl cryno	ICMLA 2010
Gwlad/Tiriogaeth	Unol Daleithiau America
Dinas	Washington DC
Cyfnod	12 Rhag 2010 → 14 Rhag 2010

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10.1109/ICMLA.2010.82

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@conference{665eb7c80a7e47cca4f14197d9204af5,

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 = "Rezwan, \{Faisal I\} and Yi Sun and Neil Davey and Rod Adams and Rust, \{Alistair G.\} and Mark Robinson",

year = "2010",

month = dec,

day = "12",

doi = "10.1109/ICMLA.2010.82",

language = "English",

pages = "523--527",

note = "9th IEEE International Conference on Machine Learning and Applications (ICMLA) 2010, ICMLA 2010 ; Conference date: 12-12-2010 Through 14-12-2010",

}

Rezwan, FI, Sun, Y, Davey, N, Adams, R, Rust, AG & Robinson, M 2010, 'Using randomised vectors in transcription factor binding site predictions', Papur a gyflwynwyd yn 9th IEEE International Conference on Machine Learning and Applications (ICMLA) 2010, Washington DC, Unol Daleithiau America, 12 Rhag 2010 - 14 Rhag 2010 tt. 523-527. https://doi.org/10.1109/ICMLA.2010.82

TY - CONF

T1 - Using randomised vectors in transcription factor binding site predictions

AU - Rezwan, Faisal I

AU - Sun, Yi

AU - Davey, Neil

AU - Adams, Rod

AU - Rust, Alistair G.

AU - Robinson, Mark

PY - 2010/12/12

Y1 - 2010/12/12

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

KW - Vector machines

UR - https://www.scopus.com/pages/publications/79952431294

U2 - 10.1109/ICMLA.2010.82

DO - 10.1109/ICMLA.2010.82

M3 - Paper

SP - 523

EP - 527

T2 - 9th IEEE International Conference on Machine Learning and Applications (ICMLA) 2010

Y2 - 12 December 2010 through 14 December 2010

ER -

Using randomised vectors in transcription factor binding site predictions

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