A Label Extended Semi-supervised Learning Method for Drug-target Interaction Prediction
Jie Zhao, Zhi Cao
Available Online April 2015.
- https://doi.org/10.2991/amcce-15.2015.292How to use a DOI?
- Data mining; Drug-target prediction; Label extension; Semi-supervised learning
- 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.
- 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 -