A unified multitask architecture for predicting local protein properties.
Submitted by noble on Tue, 2012-05-22 07:08
| Title | A unified multitask architecture for predicting local protein properties. |
| Publication Type | Journal Article |
| Year of Publication | 2012 |
| Authors | Qi Y, Oja M, Weston J, Noble W S |
| Journal | PloS one |
| Volume | 7 |
| Issue | 3 |
| Pagination | e32235 |
| Date or Month Published | 2012 |
| ISSN | 1932-6203 |
| Abstract | A variety of functionally important protein properties, such as secondary structure, transmembrane topology and solvent accessibility, can be encoded as a labeling of amino acids. Indeed, the prediction of such properties from the primary amino acid sequence is one of the core projects of computational biology. Accordingly, a panoply of approaches have been developed for predicting such properties; however, most such approaches focus on solving a single task at a time. Motivated by recent, successful work in natural language processing, we propose to use multitask learning to train a single, joint model that exploits the dependencies among these various labeling tasks. We describe a deep neural network architecture that, given a protein sequence, outputs a host of predicted local properties, including secondary structure, solvent accessibility, transmembrane topology, signal peptides and DNA-binding residues. The network is trained jointly on all these tasks in a supervised fashion, augmented with a novel form of semi-supervised learning in which the model is trained to distinguish between local patterns from natural and synthetic protein sequences. The task-independent architecture of the network obviates the need for task-specific feature engineering. We demonstrate that, for all of the tasks that we considered, our approach leads to statistically significant improvements in performance, relative to a single task neural network approach, and that the resulting model achieves state-of-the-art performance. |
| DOI | 10.1371/journal.pone.0032235 |
| Downloads | |
| Alternate Journal | PLoS ONE |
| Citation Key | 7851 |
Last changed Tue, 2012-05-22 07:20

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