A deep neural network witharestricted noisy channel for identification of functional introns

Alan Joseph Bekker; Michal Chorev; Liran Carmel; Jacob Goldberger

doi:10.1109/mlsp.2017.8168186

A deep neural network witharestricted noisy channel for identification of functional introns

Alan Joseph Bekker, Michal Chorev, Liran Carmel, Jacob Goldberger

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

An appreciable fraction of introns is thought to be involved in cellular functions, but there is no obvious way to predict which specific intron is likely to be functional. For each intron we are given a feature representation that is based on its evolutionary patterns. For a small subsets of introns we are also given an indication that they are functional. For all other introns it is not known whether they are functional or not. Our task is to estimate what fraction of introns are functional and, how likely it is that each individual intron is functional. We define a probabilistic classification model that treats the given functionality labels as noisy versions of labels created by a Deep Neural Network model. The maximum-likelihood model parameters are found by utilizing the Expectation-Maximization algorithm. We show that roughly 80% of the functional introns are still not recognized as such, and that roughly a third of all introns are functional.

Original language	English
Title of host publication	2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Proceedings
Editors	Naonori Ueda, Jen-Tzung Chien, Tomoko Matsui, Jan Larsen, Shinji Watanabe
Publisher	IEEE Computer Society
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781509063413
DOIs	https://doi.org/10.1109/mlsp.2017.8168186
State	Published - 5 Dec 2017
Event	2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Tokyo, Japan Duration: 25 Sep 2017 → 28 Sep 2017

Publication series

Name	IEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume	2017-September
ISSN (Print)	2161-0363
ISSN (Electronic)	2161-0371

Conference

Conference	2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017
Country/Territory	Japan
City	Tokyo
Period	25/09/17 → 28/09/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

Intron function
Noisy labels
Semi-supervised
Uncertain labels

Access to Document

10.1109/mlsp.2017.8168186

Cite this

Bekker, A. J., Chorev, M., Carmel, L., & Goldberger, J. (2017). A deep neural network witharestricted noisy channel for identification of functional introns. In N. Ueda, J.-T. Chien, T. Matsui, J. Larsen, & S. Watanabe (Eds.), 2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Proceedings (pp. 1-6). (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol. 2017-September). IEEE Computer Society. https://doi.org/10.1109/mlsp.2017.8168186

Bekker, Alan Joseph ; Chorev, Michal ; Carmel, Liran et al. / A deep neural network witharestricted noisy channel for identification of functional introns. 2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Proceedings. editor / Naonori Ueda ; Jen-Tzung Chien ; Tomoko Matsui ; Jan Larsen ; Shinji Watanabe. IEEE Computer Society, 2017. pp. 1-6 (IEEE International Workshop on Machine Learning for Signal Processing, MLSP).

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abstract = "An appreciable fraction of introns is thought to be involved in cellular functions, but there is no obvious way to predict which specific intron is likely to be functional. For each intron we are given a feature representation that is based on its evolutionary patterns. For a small subsets of introns we are also given an indication that they are functional. For all other introns it is not known whether they are functional or not. Our task is to estimate what fraction of introns are functional and, how likely it is that each individual intron is functional. We define a probabilistic classification model that treats the given functionality labels as noisy versions of labels created by a Deep Neural Network model. The maximum-likelihood model parameters are found by utilizing the Expectation-Maximization algorithm. We show that roughly 80% of the functional introns are still not recognized as such, and that roughly a third of all introns are functional.",

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Bekker, AJ, Chorev, M, Carmel, L & Goldberger, J 2017, A deep neural network witharestricted noisy channel for identification of functional introns. in N Ueda, J-T Chien, T Matsui, J Larsen & S Watanabe (eds), 2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Proceedings. IEEE International Workshop on Machine Learning for Signal Processing, MLSP, vol. 2017-September, IEEE Computer Society, pp. 1-6, 2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017, Tokyo, Japan, 25/09/17. https://doi.org/10.1109/mlsp.2017.8168186

A deep neural network witharestricted noisy channel for identification of functional introns. / Bekker, Alan Joseph; Chorev, Michal; Carmel, Liran et al.
2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Proceedings. ed. / Naonori Ueda; Jen-Tzung Chien; Tomoko Matsui; Jan Larsen; Shinji Watanabe. IEEE Computer Society, 2017. p. 1-6 (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol. 2017-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Bekker AJ, Chorev M, Carmel L, Goldberger J. A deep neural network witharestricted noisy channel for identification of functional introns. In Ueda N, Chien JT, Matsui T, Larsen J, Watanabe S, editors, 2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Proceedings. IEEE Computer Society. 2017. p. 1-6. (IEEE International Workshop on Machine Learning for Signal Processing, MLSP). doi: 10.1109/mlsp.2017.8168186

A deep neural network witharestricted noisy channel for identification of functional introns

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Bibliographical note

Keywords

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