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Predicting gene expression levels from histone modification signals with convolutional recurrent neural networks

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Standard

Predicting gene expression levels from histone modification signals with convolutional recurrent neural networks. / Zhu, Lingyu; Kesseli, Juha; Nykter, Matti; Huttunen, Heikki.

EMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017. Springer Verlag, 2018. p. 555-558 (IFMBE Proceedings; Vol. 65).

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Zhu, L, Kesseli, J, Nykter, M & Huttunen, H 2018, Predicting gene expression levels from histone modification signals with convolutional recurrent neural networks. in EMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017. IFMBE Proceedings, vol. 65, Springer Verlag, pp. 555-558, Joint Conference of the European Medical and Biological Engineering Conference (EMBEC) and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics (NBC), 1/01/00. https://doi.org/10.1007/978-981-10-5122-7_139

APA

Zhu, L., Kesseli, J., Nykter, M., & Huttunen, H. (2018). Predicting gene expression levels from histone modification signals with convolutional recurrent neural networks. In EMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017 (pp. 555-558). (IFMBE Proceedings; Vol. 65). Springer Verlag. https://doi.org/10.1007/978-981-10-5122-7_139

Vancouver

Zhu L, Kesseli J, Nykter M, Huttunen H. Predicting gene expression levels from histone modification signals with convolutional recurrent neural networks. In EMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017. Springer Verlag. 2018. p. 555-558. (IFMBE Proceedings). https://doi.org/10.1007/978-981-10-5122-7_139

Author

Zhu, Lingyu ; Kesseli, Juha ; Nykter, Matti ; Huttunen, Heikki. / Predicting gene expression levels from histone modification signals with convolutional recurrent neural networks. EMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017. Springer Verlag, 2018. pp. 555-558 (IFMBE Proceedings).

Bibtex - Download

@inproceedings{7a9641db22a9475f8c42f8460a6284f4,
title = "Predicting gene expression levels from histone modification signals with convolutional recurrent neural networks",
abstract = "In this paper we study how a Convolutional Recurrent Neural Network performs for predicting the gene expression levels from histone modification signals. Moreover, we consider two simplified variants of the Convolutional Recurrent Neural Network: Convolutional Neural Network and Recurrent Neural Network. The performance of the methods is evaluated with histone modification signal and gene expression data derived from Roadmap Epigenomics Mapping Consortium database, and compared against the state of the art method: the DeepChrome. It is shown that the proposed models give a statistically significant improvement over the baseline.",
keywords = "Convolutional neural networks, Convolutional recurrent neural networks, Gene expression, Histone modification, Recurrent neural networks",
author = "Lingyu Zhu and Juha Kesseli and Matti Nykter and Heikki Huttunen",
note = "jufoid=58152 INT=sgn,{"}Zhu, Lingyu{"} EXT={"}Kesseli, Juha{"} EXT={"}Nykter, Matti{"}",
year = "2018",
doi = "10.1007/978-981-10-5122-7_139",
language = "English",
isbn = "9789811051210",
series = "IFMBE Proceedings",
publisher = "Springer Verlag",
pages = "555--558",
booktitle = "EMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017",
address = "Germany",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Predicting gene expression levels from histone modification signals with convolutional recurrent neural networks

AU - Zhu, Lingyu

AU - Kesseli, Juha

AU - Nykter, Matti

AU - Huttunen, Heikki

N1 - jufoid=58152 INT=sgn,"Zhu, Lingyu" EXT="Kesseli, Juha" EXT="Nykter, Matti"

PY - 2018

Y1 - 2018

N2 - In this paper we study how a Convolutional Recurrent Neural Network performs for predicting the gene expression levels from histone modification signals. Moreover, we consider two simplified variants of the Convolutional Recurrent Neural Network: Convolutional Neural Network and Recurrent Neural Network. The performance of the methods is evaluated with histone modification signal and gene expression data derived from Roadmap Epigenomics Mapping Consortium database, and compared against the state of the art method: the DeepChrome. It is shown that the proposed models give a statistically significant improvement over the baseline.

AB - In this paper we study how a Convolutional Recurrent Neural Network performs for predicting the gene expression levels from histone modification signals. Moreover, we consider two simplified variants of the Convolutional Recurrent Neural Network: Convolutional Neural Network and Recurrent Neural Network. The performance of the methods is evaluated with histone modification signal and gene expression data derived from Roadmap Epigenomics Mapping Consortium database, and compared against the state of the art method: the DeepChrome. It is shown that the proposed models give a statistically significant improvement over the baseline.

KW - Convolutional neural networks

KW - Convolutional recurrent neural networks

KW - Gene expression

KW - Histone modification

KW - Recurrent neural networks

U2 - 10.1007/978-981-10-5122-7_139

DO - 10.1007/978-981-10-5122-7_139

M3 - Conference contribution

SN - 9789811051210

T3 - IFMBE Proceedings

SP - 555

EP - 558

BT - EMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017

PB - Springer Verlag

ER -