Integrating multi-scale gene features for cancer diagnosis

Peng Hang; Mengjun Shi; Quan Long; Hui Li; Haifeng Zhao; Meng Ma

doi:10.1007/978-3-319-97909-0_67

Integrating multi-scale gene features for cancer diagnosis

Peng Hang
, Mengjun Shi
, Quan Long
, Hui Li
, Haifeng Zhao
, Meng Ma

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

Abstract

Cancer is one of the major diseases that threaten human life. The advancement of high-throughput sequencing technology provides a way to accurately diagnose cancer and reveal the pathogenesis of cancer at the molecular level. In this study, we integrated the differentially expressed genes, and differential DNA methylation patterns, and applied multiple machine learning methods to conduct cancer diagnosis. The experimental results show that the performance of cancer diagnosis can be significantly improved with the integrated multi-scale gene features of RNA and epigenetic level. The AUC of classifier can be increased by 7.4% with multi-scale gene features compared to only differentially expressed genes, which verifies the effectiveness of the integration of multi-scale gene features for cancer diagnosis.

Original language	English
Title of host publication	Biometric Recognition - 13th Chinese Conference, CCBR 2018, Proceedings
Editors	Zhenan Sun, Shiguang Shan, Zhenhong Jia, Kurban Ubul, Jie Zhou, Jianjiang Feng, Zhenhua Guo, Yunhong Wang
Publisher	Springer Verlag
Pages	632-641
Number of pages	10
ISBN (Print)	9783319979083
DOIs	https://doi.org/10.1007/978-3-319-97909-0_67
State	Published - 2018
Externally published	Yes
Event	13th Chinese Conference on Biometric Recognition, CCBR 2018 - Urumchi, China Duration: 11 Aug 2018 → 12 Aug 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10996 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th Chinese Conference on Biometric Recognition, CCBR 2018
Country/Territory	China
City	Urumchi
Period	11/08/18 → 12/08/18

Keywords

Cancer diagnosis
DNA methylation
Gene expression
High-Throughput sequencing technology
Machine learning

Access to Document

10.1007/978-3-319-97909-0_67

Cite this

Hang, P., Shi, M., Long, Q., Li, H., Zhao, H., & Ma, M. (2018). Integrating multi-scale gene features for cancer diagnosis. In Z. Sun, S. Shan, Z. Jia, K. Ubul, J. Zhou, J. Feng, Z. Guo, & Y. Wang (Eds.), Biometric Recognition - 13th Chinese Conference, CCBR 2018, Proceedings (pp. 632-641). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10996 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-97909-0_67

Hang, Peng ; Shi, Mengjun ; Long, Quan et al. / Integrating multi-scale gene features for cancer diagnosis. Biometric Recognition - 13th Chinese Conference, CCBR 2018, Proceedings. editor / Zhenan Sun ; Shiguang Shan ; Zhenhong Jia ; Kurban Ubul ; Jie Zhou ; Jianjiang Feng ; Zhenhua Guo ; Yunhong Wang. Springer Verlag, 2018. pp. 632-641 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{b85df183cd154e51b6ce649ee7b96f97,

title = "Integrating multi-scale gene features for cancer diagnosis",

abstract = "Cancer is one of the major diseases that threaten human life. The advancement of high-throughput sequencing technology provides a way to accurately diagnose cancer and reveal the pathogenesis of cancer at the molecular level. In this study, we integrated the differentially expressed genes, and differential DNA methylation patterns, and applied multiple machine learning methods to conduct cancer diagnosis. The experimental results show that the performance of cancer diagnosis can be significantly improved with the integrated multi-scale gene features of RNA and epigenetic level. The AUC of classifier can be increased by 7.4\% with multi-scale gene features compared to only differentially expressed genes, which verifies the effectiveness of the integration of multi-scale gene features for cancer diagnosis.",

keywords = "Cancer diagnosis, DNA methylation, Gene expression, High-Throughput sequencing technology, Machine learning",

author = "Peng Hang and Mengjun Shi and Quan Long and Hui Li and Haifeng Zhao and Meng Ma",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 13th Chinese Conference on Biometric Recognition, CCBR 2018 ; Conference date: 11-08-2018 Through 12-08-2018",

year = "2018",

doi = "10.1007/978-3-319-97909-0\_67",

language = "English",

isbn = "9783319979083",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "632--641",

editor = "Zhenan Sun and Shiguang Shan and Zhenhong Jia and Kurban Ubul and Jie Zhou and Jianjiang Feng and Zhenhua Guo and Yunhong Wang",

booktitle = "Biometric Recognition - 13th Chinese Conference, CCBR 2018, Proceedings",

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}

Hang, P, Shi, M, Long, Q, Li, H, Zhao, H & Ma, M 2018, Integrating multi-scale gene features for cancer diagnosis. in Z Sun, S Shan, Z Jia, K Ubul, J Zhou, J Feng, Z Guo & Y Wang (eds), Biometric Recognition - 13th Chinese Conference, CCBR 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10996 LNCS, Springer Verlag, pp. 632-641, 13th Chinese Conference on Biometric Recognition, CCBR 2018, Urumchi, China, 11/08/18. https://doi.org/10.1007/978-3-319-97909-0_67

Integrating multi-scale gene features for cancer diagnosis. / Hang, Peng; Shi, Mengjun; Long, Quan et al.
Biometric Recognition - 13th Chinese Conference, CCBR 2018, Proceedings. ed. / Zhenan Sun; Shiguang Shan; Zhenhong Jia; Kurban Ubul; Jie Zhou; Jianjiang Feng; Zhenhua Guo; Yunhong Wang. Springer Verlag, 2018. p. 632-641 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10996 LNCS).

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

TY - GEN

T1 - Integrating multi-scale gene features for cancer diagnosis

AU - Hang, Peng

AU - Shi, Mengjun

AU - Long, Quan

AU - Li, Hui

AU - Zhao, Haifeng

AU - Ma, Meng

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2018.

PY - 2018

Y1 - 2018

N2 - Cancer is one of the major diseases that threaten human life. The advancement of high-throughput sequencing technology provides a way to accurately diagnose cancer and reveal the pathogenesis of cancer at the molecular level. In this study, we integrated the differentially expressed genes, and differential DNA methylation patterns, and applied multiple machine learning methods to conduct cancer diagnosis. The experimental results show that the performance of cancer diagnosis can be significantly improved with the integrated multi-scale gene features of RNA and epigenetic level. The AUC of classifier can be increased by 7.4% with multi-scale gene features compared to only differentially expressed genes, which verifies the effectiveness of the integration of multi-scale gene features for cancer diagnosis.

AB - Cancer is one of the major diseases that threaten human life. The advancement of high-throughput sequencing technology provides a way to accurately diagnose cancer and reveal the pathogenesis of cancer at the molecular level. In this study, we integrated the differentially expressed genes, and differential DNA methylation patterns, and applied multiple machine learning methods to conduct cancer diagnosis. The experimental results show that the performance of cancer diagnosis can be significantly improved with the integrated multi-scale gene features of RNA and epigenetic level. The AUC of classifier can be increased by 7.4% with multi-scale gene features compared to only differentially expressed genes, which verifies the effectiveness of the integration of multi-scale gene features for cancer diagnosis.

KW - Cancer diagnosis

KW - DNA methylation

KW - Gene expression

KW - High-Throughput sequencing technology

KW - Machine learning

UR - https://www.scopus.com/pages/publications/85051962387

U2 - 10.1007/978-3-319-97909-0_67

DO - 10.1007/978-3-319-97909-0_67

M3 - Conference contribution

AN - SCOPUS:85051962387

SN - 9783319979083

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 632

EP - 641

BT - Biometric Recognition - 13th Chinese Conference, CCBR 2018, Proceedings

A2 - Sun, Zhenan

A2 - Shan, Shiguang

A2 - Jia, Zhenhong

A2 - Ubul, Kurban

A2 - Zhou, Jie

A2 - Feng, Jianjiang

A2 - Guo, Zhenhua

A2 - Wang, Yunhong

PB - Springer Verlag

T2 - 13th Chinese Conference on Biometric Recognition, CCBR 2018

Y2 - 11 August 2018 through 12 August 2018

ER -

Hang P, Shi M, Long Q, Li H, Zhao H, Ma M. Integrating multi-scale gene features for cancer diagnosis. In Sun Z, Shan S, Jia Z, Ubul K, Zhou J, Feng J, Guo Z, Wang Y, editors, Biometric Recognition - 13th Chinese Conference, CCBR 2018, Proceedings. Springer Verlag. 2018. p. 632-641. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-97909-0_67

Integrating multi-scale gene features for cancer diagnosis

Abstract

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Keywords

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