A Fast Quantum Clustering Approach for Cancer Gene Clustering

Rong Zhul; Guangshun Li; Jin Xing Liu; Ling Yun Dai; Shasha Yuan; Ying Guo

doi:10.1109/BIBM.2018.8621341

A Fast Quantum Clustering Approach for Cancer Gene Clustering

Rong Zhul
, Guangshun Li
, Jin Xing Liu
, Ling Yun Dai
, Shasha Yuan
, Ying Guo

Quffi Normal University
Central South University

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

7 Scopus citations

Abstract

At present, the main problem of gene expression data processing is how to use effective analytical methods to analyze gene expression data to obtain useful information. A large amount of useful information can be obtained by cluster analysis of gene expression data, which provides a basis for biology to predict cell cycle, predict the gene function, discover disease-causing genes and explain some new disease types. In this paper, a fast clustering method is proposed, which effectively combines the advantages of the graph regularized non-negative matrices factorization (GNMF) method with Quantum Clustering (QC) method, effectively improving the running speed of existing clustering methods and improving the clustering accuracy to some extent.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Editors	Harald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1610-1613
Number of pages	4
ISBN (Electronic)	9781538654880
DOIs	https://doi.org/10.1109/BIBM.2018.8621341
State	Published - 21 Jan 2019
Externally published	Yes
Event	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain Duration: 3 Dec 2018 → 6 Dec 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Country/Territory	Spain
City	Madrid
Period	3/12/18 → 6/12/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Cancer Gene Clustering
Fast
GNMF
QC

Access to Document

10.1109/BIBM.2018.8621341

Cite this

Zhul, R., Li, G., Liu, J. X., Dai, L. Y., Yuan, S., & Guo, Y. (2019). A Fast Quantum Clustering Approach for Cancer Gene Clustering. In H. Schmidt, D. Griol, H. Wang, J. Baumbach, H. Zheng, Z. Callejas, X. Hu, J. Dickerson, & L. Zhang (Eds.), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 (pp. 1610-1613). Article 8621341 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2018.8621341

Zhul, Rong ; Li, Guangshun ; Liu, Jin Xing et al. / A Fast Quantum Clustering Approach for Cancer Gene Clustering. Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. editor / Harald Schmidt ; David Griol ; Haiying Wang ; Jan Baumbach ; Huiru Zheng ; Zoraida Callejas ; Xiaohua Hu ; Julie Dickerson ; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1610-1613 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

@inproceedings{98254759d6e44367a7076b4b38a4cf62,

title = "A Fast Quantum Clustering Approach for Cancer Gene Clustering",

abstract = "At present, the main problem of gene expression data processing is how to use effective analytical methods to analyze gene expression data to obtain useful information. A large amount of useful information can be obtained by cluster analysis of gene expression data, which provides a basis for biology to predict cell cycle, predict the gene function, discover disease-causing genes and explain some new disease types. In this paper, a fast clustering method is proposed, which effectively combines the advantages of the graph regularized non-negative matrices factorization (GNMF) method with Quantum Clustering (QC) method, effectively improving the running speed of existing clustering methods and improving the clustering accuracy to some extent.",

keywords = "Cancer Gene Clustering, Fast, GNMF, QC",

author = "Rong Zhul and Guangshun Li and Liu, \{Jin Xing\} and Dai, \{Ling Yun\} and Shasha Yuan and Ying Guo",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 ; Conference date: 03-12-2018 Through 06-12-2018",

year = "2019",

month = jan,

day = "21",

doi = "10.1109/BIBM.2018.8621341",

language = "英语",

series = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",

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

pages = "1610--1613",

editor = "Harald Schmidt and David Griol and Haiying Wang and Jan Baumbach and Huiru Zheng and Zoraida Callejas and Xiaohua Hu and Julie Dickerson and Le Zhang",

booktitle = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",

}

Zhul, R, Li, G, Liu, JX, Dai, LY, Yuan, S & Guo, Y 2019, A Fast Quantum Clustering Approach for Cancer Gene Clustering. in H Schmidt, D Griol, H Wang, J Baumbach, H Zheng, Z Callejas, X Hu, J Dickerson & L Zhang (eds), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018., 8621341, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1610-1613, 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Madrid, Spain, 3/12/18. https://doi.org/10.1109/BIBM.2018.8621341

A Fast Quantum Clustering Approach for Cancer Gene Clustering. / Zhul, Rong; Li, Guangshun; Liu, Jin Xing et al.
Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. ed. / Harald Schmidt; David Griol; Haiying Wang; Jan Baumbach; Huiru Zheng; Zoraida Callejas; Xiaohua Hu; Julie Dickerson; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1610-1613 8621341 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

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

TY - GEN

T1 - A Fast Quantum Clustering Approach for Cancer Gene Clustering

AU - Zhul, Rong

AU - Li, Guangshun

AU - Liu, Jin Xing

AU - Dai, Ling Yun

AU - Yuan, Shasha

AU - Guo, Ying

PY - 2019/1/21

Y1 - 2019/1/21

N2 - At present, the main problem of gene expression data processing is how to use effective analytical methods to analyze gene expression data to obtain useful information. A large amount of useful information can be obtained by cluster analysis of gene expression data, which provides a basis for biology to predict cell cycle, predict the gene function, discover disease-causing genes and explain some new disease types. In this paper, a fast clustering method is proposed, which effectively combines the advantages of the graph regularized non-negative matrices factorization (GNMF) method with Quantum Clustering (QC) method, effectively improving the running speed of existing clustering methods and improving the clustering accuracy to some extent.

AB - At present, the main problem of gene expression data processing is how to use effective analytical methods to analyze gene expression data to obtain useful information. A large amount of useful information can be obtained by cluster analysis of gene expression data, which provides a basis for biology to predict cell cycle, predict the gene function, discover disease-causing genes and explain some new disease types. In this paper, a fast clustering method is proposed, which effectively combines the advantages of the graph regularized non-negative matrices factorization (GNMF) method with Quantum Clustering (QC) method, effectively improving the running speed of existing clustering methods and improving the clustering accuracy to some extent.

KW - Cancer Gene Clustering

KW - Fast

KW - GNMF

KW - QC

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M3 - 会议稿件

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T3 - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

SP - 1610

EP - 1613

BT - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

A2 - Schmidt, Harald

A2 - Griol, David

A2 - Wang, Haiying

A2 - Baumbach, Jan

A2 - Zheng, Huiru

A2 - Callejas, Zoraida

A2 - Hu, Xiaohua

A2 - Dickerson, Julie

A2 - Zhang, Le

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Y2 - 3 December 2018 through 6 December 2018

ER -

Zhul R, Li G, Liu JX, Dai LY, Yuan S, Guo Y. A Fast Quantum Clustering Approach for Cancer Gene Clustering. In Schmidt H, Griol D, Wang H, Baumbach J, Zheng H, Callejas Z, Hu X, Dickerson J, Zhang L, editors, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1610-1613. 8621341. (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). doi: 10.1109/BIBM.2018.8621341

A Fast Quantum Clustering Approach for Cancer Gene Clustering

Abstract

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UN SDGs

Keywords

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