Bioinformatics tools with deep learning based on GPU

Che Lun Hung; Chuan Yi Tang

doi:10.1109/BIBM.2017.8217950

Bioinformatics tools with deep learning based on GPU

Che Lun Hung, Chuan Yi Tang

Providence University Taiwan

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

8 Scopus citations

Abstract

Due to the rapid increase in biological data dimension and acquisition rate, the traditional analysis methods are unable to achieve acceptable accuracy. Recently, Deep learning technologies have shown outstanding results in many domains; especially in pattern recognition in the field of bioinformatics. In this paper, we provide background of what deep learning and its frameworks. In addition, we review the state-of-the-art algorithms based on GPU to presenting the usage of them to guide computational biologists to know how to leverage deep learning to improve their methods.

Original language	English
Title of host publication	Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Editors	Illhoi Yoo, Jane Huiru Zheng, Yang Gong, Xiaohua Tony Hu, Chi-Ren Shyu, Yana Bromberg, Jean Gao, Dmitry Korkin
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1906-1908
Number of pages	3
ISBN (Electronic)	9781509030491
DOIs	https://doi.org/10.1109/BIBM.2017.8217950
State	Published - 15 12 2017
Externally published	Yes
Event	2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 - Kansas City, United States Duration: 13 11 2017 → 16 11 2017

Publication series

Name	Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Volume	2017-January

Conference

Conference	2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Country/Territory	United States
City	Kansas City
Period	13/11/17 → 16/11/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

Bioinformatics
Deep Learning
GPU
High Performance Computing

Access to Document

10.1109/BIBM.2017.8217950

Cite this

Hung, C. L., & Tang, C. Y. (2017). Bioinformatics tools with deep learning based on GPU. In I. Yoo, J. H. Zheng, Y. Gong, X. T. Hu, C.-R. Shyu, Y. Bromberg, J. Gao, & D. Korkin (Eds.), Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 (pp. 1906-1908). (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2017.8217950

Hung, Che Lun ; Tang, Chuan Yi. / Bioinformatics tools with deep learning based on GPU. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. editor / Illhoi Yoo ; Jane Huiru Zheng ; Yang Gong ; Xiaohua Tony Hu ; Chi-Ren Shyu ; Yana Bromberg ; Jean Gao ; Dmitry Korkin. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1906-1908 (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017).

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title = "Bioinformatics tools with deep learning based on GPU",

abstract = "Due to the rapid increase in biological data dimension and acquisition rate, the traditional analysis methods are unable to achieve acceptable accuracy. Recently, Deep learning technologies have shown outstanding results in many domains; especially in pattern recognition in the field of bioinformatics. In this paper, we provide background of what deep learning and its frameworks. In addition, we review the state-of-the-art algorithms based on GPU to presenting the usage of them to guide computational biologists to know how to leverage deep learning to improve their methods.",

keywords = "Bioinformatics, Deep Learning, GPU, High Performance Computing",

author = "Hung, {Che Lun} and Tang, {Chuan Yi}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 ; Conference date: 13-11-2017 Through 16-11-2017",

year = "2017",

month = dec,

day = "15",

doi = "10.1109/BIBM.2017.8217950",

language = "英语",

series = "Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1906--1908",

editor = "Illhoi Yoo and Zheng, {Jane Huiru} and Yang Gong and Hu, {Xiaohua Tony} and Chi-Ren Shyu and Yana Bromberg and Jean Gao and Dmitry Korkin",

booktitle = "Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017",

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Hung, CL & Tang, CY 2017, Bioinformatics tools with deep learning based on GPU. in I Yoo, JH Zheng, Y Gong, XT Hu, C-R Shyu, Y Bromberg, J Gao & D Korkin (eds), Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 1906-1908, 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, Kansas City, United States, 13/11/17. https://doi.org/10.1109/BIBM.2017.8217950

Bioinformatics tools with deep learning based on GPU. / Hung, Che Lun; Tang, Chuan Yi.
Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. ed. / Illhoi Yoo; Jane Huiru Zheng; Yang Gong; Xiaohua Tony Hu; Chi-Ren Shyu; Yana Bromberg; Jean Gao; Dmitry Korkin. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1906-1908 (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017; Vol. 2017-January).

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

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AB - Due to the rapid increase in biological data dimension and acquisition rate, the traditional analysis methods are unable to achieve acceptable accuracy. Recently, Deep learning technologies have shown outstanding results in many domains; especially in pattern recognition in the field of bioinformatics. In this paper, we provide background of what deep learning and its frameworks. In addition, we review the state-of-the-art algorithms based on GPU to presenting the usage of them to guide computational biologists to know how to leverage deep learning to improve their methods.

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A2 - Zheng, Jane Huiru

A2 - Gong, Yang

A2 - Hu, Xiaohua Tony

A2 - Shyu, Chi-Ren

A2 - Bromberg, Yana

A2 - Gao, Jean

A2 - Korkin, Dmitry

PB - Institute of Electrical and Electronics Engineers Inc.

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Hung CL, Tang CY. Bioinformatics tools with deep learning based on GPU. In Yoo I, Zheng JH, Gong Y, Hu XT, Shyu CR, Bromberg Y, Gao J, Korkin D, editors, Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1906-1908. (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017). doi: 10.1109/BIBM.2017.8217950

Bioinformatics tools with deep learning based on GPU

Abstract

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Bibliographical note

Keywords

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