Protein-metaDeep (Index of Posts):

This categoy of deep learning tools design or use meta-learning strategies to analyze protein data..
This includes:


A unified multitask architecture for predicting local protein properties

Tool Multitask-ProteinTagging: A unified multitask architecture for predicting local protein properties

Paper

GitHub

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.

multi

multi

Citations

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

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Deep Learning for Character-based Information Extraction on Chinese and Protein Sequence

Title: Deep Learning for Character-based Information Extraction on Chinese and Protein Sequence

  • authors: Yanjun Qi, Sujatha Das, Ronan Collobert, Jason Weston

Paper ECIR

Supplementary Here

Talk: Slide

Abstract

In this paper we introduce a deep neural network architecture to perform information extraction on character-based sequences, e.g. named-entity recognition on Chinese text or secondary-structure detection on protein sequences. With a task-independent architecture, the deep network relies only on simple character-based features, which obviates the need for task-specific feature engineering. The proposed discriminative framework includes three important strategies, (1) a deep learning module mapping characters to vector representations is included to capture the semantic relationship between characters; (2) abundant online sequences (unlabeled) are utilized to improve the vector representation through semi-supervised learning; and (3) the constraints of spatial dependency among output labels are modeled explicitly in the deep architecture. The experiments on four benchmark datasets have demonstrated that, the proposed architecture consistently leads to the state-of-the-art performance.

Citations

@inproceedings{qi2014deep,
  title={Deep learning for character-based information extraction},
  author={Qi, Yanjun and Das, Sujatha G and Collobert, Ronan and Weston, Jason},
  booktitle={European Conference on Information Retrieval},
  pages={668--674},
  year={2014},
  organization={Springer}
}

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Semi-supervised multi-task learning Using BioText based Labels to Augument PPI Prediction

Title: Semi-supervised multi-task learning for predicting interactions between HIV-1 and human proteins

  • authors: Yanjun Qi, Oznur Tastan, Jaime G. Carbonell, Judith Klein-Seetharaman, Jason Weston

Paper Bioinformatics

Talk: Slide

Abstract

  • Motivation: Protein–protein interactions (PPIs) are critical for virtually every biological function. Recently, researchers suggested to use supervised learning for the task of classifying pairs of proteins as interacting or not. However, its performance is largely restricted by the availability of truly interacting proteins (labeled). Meanwhile, there exists a considerable amount of protein pairs where an association appears between two partners, but not enough experimental evidence to support it as a direct interaction (partially labeled).

  • Results: We propose a semi-supervised multi-task framework for predicting PPIs from not only labeled, but also partially labeled reference sets. The basic idea is to perform multi-task learning on a supervised classification task and a semi-supervised auxiliary task. The supervised classifier trains a multi-layer perceptron network for PPI predictions from labeled examples. The semi-supervised auxiliary task shares network layers of the supervised classifier and trains with partially labeled examples. Semi-supervision could be utilized in multiple ways. We tried three approaches in this article, (i) classification (to distinguish partial positives with negatives); (ii) ranking (to rate partial positive more likely than negatives); (iii) embedding (to make data clusters get similar labels). We applied this framework to improve the identification of interacting pairs between HIV-1 and human proteins. Our method improved upon the state-of-the-art method for this task indicating the benefits of semi-supervised multi-task learning using auxiliary information.

Citations

@article{qi2010semi,
  title={Semi-supervised multi-task learning for predicting interactions between HIV-1 and human proteins},
  author={Qi, Yanjun and Tastan, Oznur and Carbonell, Jaime G and Klein-Seetharaman, Judith and Weston, Jason},
  journal={Bioinformatics},
  volume={26},
  number={18},
  pages={i645--i652},
  year={2010},
  publisher={Oxford University Press}
}

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