Machine Learning - Special issue on inductive transfer
Learning to learn
A clustering algorithm based on graph connectivity
Information Processing Letters
A Hierarchical Bayes Model of Primary and Secondary Demand
Marketing Science
Analysis and visualization of gene expression data using self-organizing maps
Neural Networks - New developments in self-organizing maps
Machine Learning
Task clustering and gating for bayesian multitask learning
The Journal of Machine Learning Research
Microarray data mining: facing the challenges
ACM SIGKDD Explorations Newsletter
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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
On Learning Vector-Valued Functions
Neural Computation
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
Multi-task learning for HIV therapy screening
Proceedings of the 25th international conference on Machine learning
Microarray-based classification and clinical predictors
Bioinformatics
An Improved Multi-task Learning Approach with Applications in Medical Diagnosis
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
A model of inductive bias learning
Journal of Artificial Intelligence Research
Clinical and molecular models of Glioblastoma multiforme survival
International Journal of Data Mining and Bioinformatics
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Gene-expression data are now widely available and used for a wide range of clinical and diagnostic purposes. A key challenge is to select a few significant marker genes for biological studies. While it is feasible to find important genes from a single gene-expression data set, it is often more meaningful to compare the results from different but related data sets together, especially for multiple gene-expression data sets arising from different studies of a common organism or phenotype. In this paper, we present a novel framework to exploit the commonalities across different data sets by jointly learning from different data sets simultaneously through multi-task feature learning. By identifying a common subspace of genes, we can help biologists find important marker genes that span different evolutionary periods in the life cycle of cancer development. The genes thus found are more stable and more significant. Our experimental results demonstrate that more accurate models can be built using multiple data sets based on fewer labelled examples. To the best of our knowledge, we are among the first to introduce multi-task learning in the bioinformatics community to solve the lack of data problem.