A transfer learning approach via procrustes analysis and mean shift for cancer drug sensitivity prediction.

A transfer learning approach via procrustes analysis and mean shift for cancer drug sensitivity prediction. J Bioinform Comput Biol. 2018 Jun;16(3):1840014 Authors: Turki T, Wei Z, Wang JTL Abstract Transfer learning (TL) algorithms aim to improve the prediction performance in a target task (e.g. the prediction of cisplatin sensitivity in triple-negative breast cancer patients) via transferring knowledge from auxiliary data of a related task (e.g. the prediction of docetaxel sensitivity in breast cancer patients), where the distribution and even the feature space of the data pertaining to the tasks can be different. In real-world applications, we sometimes have a limited training set in a target task while we have auxiliary data from a related task. To obtain a better prediction performance in the target task, supervised learning requires a sufficiently large training set in the target task to perform well in predicting future test examples of the target task. In this paper, we propose a TL approach for cancer drug sensitivity prediction, where our approach combines three techniques. First, we shift the representation of a subset of examples from auxiliary data of a related task to a representation closer to a target training set of a target task. Second, we align the shifted representation of the selected examples of the auxiliary data to the target training set to obtain examples with representation aligned to the target training s...
Source: Journal of Bioinformatics and Computational Biology - Category: Bioinformatics Authors: Tags: J Bioinform Comput Biol Source Type: research