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Predicting Signal Peptides using Deep Neural Networks

Predicting Signal Peptides using Deep Neural Networks. Cecilie Anker, Casper Sønderby and Søren Sønderby 02459 MACHINE LEARNING FOR SIGNAL PROCESSING, DTU COMPUTE, SPRING 2013 . Purpose. Identify Cleavage sites in signal peptides

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Predicting Signal Peptides using Deep Neural Networks

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  1. Predicting Signal Peptides using Deep Neural Networks Cecilie Anker, Casper Sønderby and Søren Sønderby 02459 MACHINE LEARNING FOR SIGNAL PROCESSING, DTU COMPUTE, SPRING 2013

  2. Purpose • Identify Cleavage sites in signal peptides • Compare SignalP 4.0 neural networks with deep neural networks

  3. Cleavage Site TTGNGLFINESAKLVDTFLEDVKNLHHSKAFSINFRDAEEAK SSSSSSSSSSSSSSSC....................TTTT.. Slide window across sequence ENCODING ...000001000010000000000001000000010000000001... Neural network Input window

  4. Dataset • SignalP 4.0 data [1] • With Signal peptides (n = 1640) • Nuclear (n = 5133) • With Transmembrane region (n = 687)

  5. Dataset Samples [thousands]

  6. Encodings

  7. Prediction Model DNN 5-Ensemble HMM-DNN hybrid Model DNN model output

  8. Ensemble Example

  9. Training • Backpropagation • Dropout • L2-norm regularization • Early stopping • Decaying learning rate • Momentum • Minibatches • No Pretraining

  10. Further work • ReLU • Maxout networks • DBN + Pretraining + SVM

  11. Results

  12. Resources • Deeplearntoolbox (Matlab) • Deeplearntoolbox GPU (Matlab) • Matlab script DTU servers • Theano (Python) • Theano script for DTU servers • Theano tutorial/examples • Python GPU matrix operation (cudamat) • Pylearn2 • Questions: Skaaesonderby@gmail.com

  13. References [1] Petersen, TN., et. al. (2011) SignalP4.0: discriminating signal peptides from transmembraneregions. Nature methods 10(8) ,785-786. [2] Hinton, GE., et al. (2012) Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580. [3] Qian, N., et. al. (1988). Predicting the secondary structure of globular proteins using neural network models. Journal of molecular biology, 202(4), 865-884. [4] Nanni, Let. al. (2011). A new encoding technique for peptide classification. Expert Systems with Applications, 38(4), 3185-3191. [5] Wu, C. H et. al. (Eds.). (2000). Neural networks and genome informatics (Vol. 1). Elsevier Science. [6] Zamani, M., et al. (2011). Amino acid encoding schemes for machine learning methods. In Bioinformatics and Biomedicine Workshops (BIBMW), 2011 IEEE International Conference on (pp. 327-333). IEEE. [7] Bourlard, H et. al, (1994), Connectionistspeechrecognition: a hybridapproach. Springer. [8] Palm, RB. (2012), Prediction as a candidate for learning deep hierarchical models of data, master thesis.

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