Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
The nature of statistical learning theory
The nature of statistical learning theory
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
Learning Gaussian processes from multiple tasks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Multi-task learning for sequential data via iHMMs and the nested Dirichlet process
Proceedings of the 24th international conference on Machine learning
A Multitask Learning Approach to Face Recognition Based on Neural Networks
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
| Hi-index | 0.00 |
The identification of small potent compounds that selectively bind to the target under consideration with high affinities is a critical step towards successful drug discovery. However, there still lacks efficient and accurate computational methods to predict compound selectivity properties. In this paper, we propose a set of machine learning methods to do compound selectivity prediction. In particular, we propose a novel cascaded learning method and a multi-task learning method. The cascaded method decomposes the selectivity prediction into two steps, one model for each step, so as to effectively filter out non-selective compounds. The multi-task method incorporates both activity and selectivity models into one multi-task model so as to better differentiate compound selectivity properties. We conducted a comprehensive set of experiments and compared the results with other conventional selectivity prediction methods, and our results demonstrated that the cascaded and multi-task methods significantly improve the selectivity prediction performance.