Machine Learning - Special issue on inductive transfer
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression
The Journal of Machine Learning Research
Multi-task learning for HIV therapy screening
Proceedings of the 25th international conference on Machine learning
Protein-ligand interaction prediction
Bioinformatics
Linear dimensionality reduction for multi-label classification
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Multi-label boosting for image annotation by structural grouping sparsity
Proceedings of the international conference on Multimedia
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Multitask Learning for Protein Subcellular Location Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Interactions between proteins and small-molecule chemicals modulate many protein functions and biological processes, and identifying these interactions is a crucial step in modern drug discovery. Supervised learning methods for predicting protein-chemical interactions (PCI) have been widely studied, but their performance is largely limited by insufficient availability of binding data for many proteins. In addition, many complex diseases such as Alzheimer's disease and cancers are found associated with multiple target proteins. Chemicals that selectively modulate only one of these target proteins are unable to effectively conquer these diseases. In this paper we propose two multi-task learning (MTL) algorithms for predicting active compounds of multiple proteins related to the same diseases, some of which may have very few binding examples. In the first method we optimize the likelihood of compound features with a Gaussian prior, while the second method boosts compound features using a number of independent boosting classifiers. Experimental studies demonstrate significant performance improvement of our MTL methods over baseline methods. Our MTL methods are also able to accurately identify promiscuous compounds that interact with multiple related proteins.