Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions

Taehoon Lee, Sungroh Yoon
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2483-2492, 2015.

Abstract

Splicing refers to the elimination of non-coding regions in transcribed pre-messenger ribonucleic acid (RNA). Discovering splice sites is an important machine learning task that helps us not only to identify the basic units of genetic heredity but also to understand how different proteins are produced. Existing methods for splicing prediction have produced promising results, but often show limited robustness and accuracy. In this paper, we propose a deep belief network-based methodology for computational splice junction prediction. Our proposal includes a novel method for training restricted Boltzmann machines for class-imbalanced prediction. The proposed method addresses the limitations of conventional contrastive divergence and provides regularization for datasets that have categorical features. We tested our approach using public human genome datasets and obtained significantly improved accuracy and reduced runtime compared to state-of-the-art alternatives. The proposed approach was less sensitive to the length of input sequences and more robust for handling false splicing signals. Furthermore, we could discover non-canonical splicing patterns that were otherwise difficult to recognize using conventional methods. Given the efficiency and robustness of our methodology, we anticipate that it can be extended to the discovery of primary structural patterns of other subtle genomic elements.

Cite this Paper


BibTeX
@InProceedings{pmlr-v37-leeb15, title = {Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions}, author = {Lee, Taehoon and Yoon, Sungroh}, booktitle = {Proceedings of the 32nd International Conference on Machine Learning}, pages = {2483--2492}, year = {2015}, editor = {Bach, Francis and Blei, David}, volume = {37}, series = {Proceedings of Machine Learning Research}, address = {Lille, France}, month = {07--09 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v37/leeb15.pdf}, url = { http://proceedings.mlr.press/v37/leeb15.html }, abstract = {Splicing refers to the elimination of non-coding regions in transcribed pre-messenger ribonucleic acid (RNA). Discovering splice sites is an important machine learning task that helps us not only to identify the basic units of genetic heredity but also to understand how different proteins are produced. Existing methods for splicing prediction have produced promising results, but often show limited robustness and accuracy. In this paper, we propose a deep belief network-based methodology for computational splice junction prediction. Our proposal includes a novel method for training restricted Boltzmann machines for class-imbalanced prediction. The proposed method addresses the limitations of conventional contrastive divergence and provides regularization for datasets that have categorical features. We tested our approach using public human genome datasets and obtained significantly improved accuracy and reduced runtime compared to state-of-the-art alternatives. The proposed approach was less sensitive to the length of input sequences and more robust for handling false splicing signals. Furthermore, we could discover non-canonical splicing patterns that were otherwise difficult to recognize using conventional methods. Given the efficiency and robustness of our methodology, we anticipate that it can be extended to the discovery of primary structural patterns of other subtle genomic elements.} }
Endnote
%0 Conference Paper %T Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions %A Taehoon Lee %A Sungroh Yoon %B Proceedings of the 32nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2015 %E Francis Bach %E David Blei %F pmlr-v37-leeb15 %I PMLR %P 2483--2492 %U http://proceedings.mlr.press/v37/leeb15.html %V 37 %X Splicing refers to the elimination of non-coding regions in transcribed pre-messenger ribonucleic acid (RNA). Discovering splice sites is an important machine learning task that helps us not only to identify the basic units of genetic heredity but also to understand how different proteins are produced. Existing methods for splicing prediction have produced promising results, but often show limited robustness and accuracy. In this paper, we propose a deep belief network-based methodology for computational splice junction prediction. Our proposal includes a novel method for training restricted Boltzmann machines for class-imbalanced prediction. The proposed method addresses the limitations of conventional contrastive divergence and provides regularization for datasets that have categorical features. We tested our approach using public human genome datasets and obtained significantly improved accuracy and reduced runtime compared to state-of-the-art alternatives. The proposed approach was less sensitive to the length of input sequences and more robust for handling false splicing signals. Furthermore, we could discover non-canonical splicing patterns that were otherwise difficult to recognize using conventional methods. Given the efficiency and robustness of our methodology, we anticipate that it can be extended to the discovery of primary structural patterns of other subtle genomic elements.
RIS
TY - CPAPER TI - Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions AU - Taehoon Lee AU - Sungroh Yoon BT - Proceedings of the 32nd International Conference on Machine Learning DA - 2015/06/01 ED - Francis Bach ED - David Blei ID - pmlr-v37-leeb15 PB - PMLR DP - Proceedings of Machine Learning Research VL - 37 SP - 2483 EP - 2492 L1 - http://proceedings.mlr.press/v37/leeb15.pdf UR - http://proceedings.mlr.press/v37/leeb15.html AB - Splicing refers to the elimination of non-coding regions in transcribed pre-messenger ribonucleic acid (RNA). Discovering splice sites is an important machine learning task that helps us not only to identify the basic units of genetic heredity but also to understand how different proteins are produced. Existing methods for splicing prediction have produced promising results, but often show limited robustness and accuracy. In this paper, we propose a deep belief network-based methodology for computational splice junction prediction. Our proposal includes a novel method for training restricted Boltzmann machines for class-imbalanced prediction. The proposed method addresses the limitations of conventional contrastive divergence and provides regularization for datasets that have categorical features. We tested our approach using public human genome datasets and obtained significantly improved accuracy and reduced runtime compared to state-of-the-art alternatives. The proposed approach was less sensitive to the length of input sequences and more robust for handling false splicing signals. Furthermore, we could discover non-canonical splicing patterns that were otherwise difficult to recognize using conventional methods. Given the efficiency and robustness of our methodology, we anticipate that it can be extended to the discovery of primary structural patterns of other subtle genomic elements. ER -
APA
Lee, T. & Yoon, S.. (2015). Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions. Proceedings of the 32nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 37:2483-2492 Available from http://proceedings.mlr.press/v37/leeb15.html .

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