A Deep-Learning based Multi-Task Framework for Protein Sequence Labeling / Local Structural Property Prediction on biological Sequences
Lua C Python CMake
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Failed to load latest commit information.



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/