Using real-valued metaclassifiers to integrate binding site predictions

Yi Sun; M. Robinson; Roderick Adams; Paul H. Kaye; A.G. Rust; N. Davey

Using real-valued metaclassifiers to integrate binding site predictions

Yi Sun, M. Robinson, Roderick Adams, Paul H. Kaye, A.G. Rust, N. Davey

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

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Abstract

Currently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. There is good reason to believe that predictions
from these different classes of algorithms could be used in conjunction to improve the quality of predictions. In this paper, we apply single layer networks, rules sets and support vector machines on predictions from 12 key real
valued algorithms. Furthermore, we use a ‘window’ of consecutive results in the input vector in order to contextualise the neighbouring results. We improve the classification result with the aid of under- and over- sampling techniques.
We find that support vector machines outperform each of the original individual algorithms and the other classifiers employed in this work. In particular they have a better tradeoff between recall and precision.

Original language	English
Title of host publication	In: Procs of IJCNN 2005, Int Joint Conference on Neural networks
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	481-486
Volume	1
Publication status	Published - 2005

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Cite this

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title = "Using real-valued metaclassifiers to integrate binding site predictions",

abstract = "Currently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. There is good reason to believe that predictions from these different classes of algorithms could be used in conjunction to improve the quality of predictions. In this paper, we apply single layer networks, rules sets and support vector machines on predictions from 12 key real valued algorithms. Furthermore, we use a {\textquoteleft}window{\textquoteright} of consecutive results in the input vector in order to contextualise the neighbouring results. We improve the classification result with the aid of under- and over- sampling techniques. We find that support vector machines outperform each of the original individual algorithms and the other classifiers employed in this work. In particular they have a better tradeoff between recall and precision.",

author = "Yi Sun and M. Robinson and Roderick Adams and Kaye, {Paul H.} and A.G. Rust and N. Davey",

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.” Original article can be found at: http://ieeexplore.ieee.org/xpl/RecentCon.jsp?punumber=10421",

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language = "English",

volume = "1",

pages = "481--486",

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T1 - Using real-valued metaclassifiers to integrate binding site predictions

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AU - Robinson, M.

AU - Adams, Roderick

AU - Kaye, Paul H.

AU - Rust, A.G.

AU - Davey, N.

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.” Original article can be found at: http://ieeexplore.ieee.org/xpl/RecentCon.jsp?punumber=10421

PY - 2005

Y1 - 2005

N2 - Currently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. There is good reason to believe that predictions from these different classes of algorithms could be used in conjunction to improve the quality of predictions. In this paper, we apply single layer networks, rules sets and support vector machines on predictions from 12 key real valued algorithms. Furthermore, we use a ‘window’ of consecutive results in the input vector in order to contextualise the neighbouring results. We improve the classification result with the aid of under- and over- sampling techniques. We find that support vector machines outperform each of the original individual algorithms and the other classifiers employed in this work. In particular they have a better tradeoff between recall and precision.

AB - Currently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. There is good reason to believe that predictions from these different classes of algorithms could be used in conjunction to improve the quality of predictions. In this paper, we apply single layer networks, rules sets and support vector machines on predictions from 12 key real valued algorithms. Furthermore, we use a ‘window’ of consecutive results in the input vector in order to contextualise the neighbouring results. We improve the classification result with the aid of under- and over- sampling techniques. We find that support vector machines outperform each of the original individual algorithms and the other classifiers employed in this work. In particular they have a better tradeoff between recall and precision.

M3 - Conference contribution

VL - 1

SP - 481

EP - 486

BT - In: Procs of IJCNN 2005, Int Joint Conference on Neural networks

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Using real-valued metaclassifiers to integrate binding site predictions

Abstract

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