qiyanjun/paper12-plosone-deepproteinmultitasktagging

A Deep-Learning based Multi-Task Framework for Protein Sequence Labeling / Local Structural Property Prediction on biological Sequences

Paper12-PlosOne-DeepProteinMultitaskTagging

Y. Qi, M. Osh, J. Weston, W. Noble (2012) A unified multitask architecture for predicting local protein properties, PLoS ONE (March 2012) (URL),

Summary: Deep neural network architecture + Protein Sequence Labeling

(bibTex), @article{qi12plosone, author = {Qi, , Yanjun AND Oja, , Merja AND Weston, , Jason AND Noble, , William Stafford}, journal = {PLoS ONE}, publisher = {Public Library of Science}, title = {A Unified Multitask Architecture for Predicting Local Protein Properties}, year = {2012}, month = {03}, volume = {7}, url = {http://dx.doi.org/10.1371%2Fjournal.pone.0032235}, pages = {e32235}, 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.

}, number = {3}, doi = {10.1371/journal.pone.0032235} }

Paper URL: http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0032235

(SupplementWeb), all trained deep models have been shared @ http://noble.gs.washington.edu/proj/multitask/models/