Proceedings of the 2015 International Conference on Automation, Mechanical Control and Computational Engineering

A Label Extended Semi-supervised Learning Method for Drug-target Interaction Prediction

Authors
Jie Zhao, Zhi Cao
Corresponding Author
Jie Zhao
Available Online April 2015.
DOI
https://doi.org/10.2991/amcce-15.2015.292How to use a DOI?
Keywords
Data mining; Drug-target prediction; Label extension; Semi-supervised learning
Abstract
Computational methods for predicting the new drug-target interactions are more efficient that those experimental methods. Many machine learning based methods have been proposed but most of them suffer from false negative problem. In this paper we extend the original label matrix and adopt weighted lose function to overcome the traditional false negative problem and then propose a label extended semi-supervised learning method called LESSL for drug-target prediction. In our experiment we use two kinds of cross-validation.The results show that our method can raise AUC average by 0.03 and raise AUPR average by 0.04. At last we use the whole dataset as a training set and predict over 10 new drug-target interactions.To conclude our method is efficient and practicable.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2015 International Conference on Automation, Mechanical Control and Computational Engineering
Part of series
Advances in Intelligent Systems Research
Publication Date
April 2015
ISBN
978-94-62520-64-6
ISSN
1951-6851
DOI
https://doi.org/10.2991/amcce-15.2015.292How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Jie Zhao
AU  - Zhi Cao
PY  - 2015/04
DA  - 2015/04
TI  - A Label Extended Semi-supervised Learning Method for Drug-target Interaction Prediction
BT  - 2015 International Conference on Automation, Mechanical Control and Computational Engineering
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/amcce-15.2015.292
DO  - https://doi.org/10.2991/amcce-15.2015.292
ID  - Zhao2015/04
ER  -