a unified multitask architecture for predicting local protein properties一个统一的多任务架构为当地预测蛋白质性质.pdfVIP

A Unified Multitask Architecture for Predicting Local Protein Properties 1 2¤ 3 2 Yanjun Qi , Merja Oja , Jason Weston , William Stafford Noble * 1 Machine Learning Department, NEC Labs America, Princeton, New Jersey, United States of America, 2 Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America, 3 Google, New York, New York, United States of America 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 enginee