Y-SPCR: A new dimensionality reduction method for gene expression data classification

Jie Li; Zhun Zhao; Li Zhou; Yadong Wang

doi:10.1109/BIBM47256.2019.8983403

Y-SPCR: A new dimensionality reduction method for gene expression data classification

Jie Li
, Zhun Zhao
, Li Zhou
, Yadong Wang

School of Computer Science and Technology, Harbin Institute of Technology

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

2 Scopus citations

Abstract

With the increase of the scale and complexity of massive data, data dimensionality reduction technologies, such as principal component analysis, have developed rapidly. The performance of dimension reduction technologies still needs to be further improved. In the paper we proposed a new dimensionality reduction method (Y-SPCR) based Supervised Principal Component Regression (SPCR) and Y-aware Principal Component Regression (Y-aware PCR). Experimental results on four gene expression data sets show that Y-SPCR effectively overcomes the shortcomings of SPCR and Y-aware PCR and improves the accuracy and stability of on gene expression data classification.

Original language	English
Title of host publication	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Editors	Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	401-408
Number of pages	8
ISBN (Electronic)	9781728118673
DOIs	https://doi.org/10.1109/BIBM47256.2019.8983403
State	Published - Nov 2019
Externally published	Yes
Event	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States Duration: 18 Nov 2019 → 21 Nov 2019

Publication series

Name	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

Conference

Conference	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Country/Territory	United States
City	San Diego
Period	18/11/19 → 21/11/19

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

SPCR
Y-SPCR
Y-aware PCR
classification
data dimension reduction
gene expression data

Access to Document

10.1109/BIBM47256.2019.8983403

Cite this

Li, J., Zhao, Z., Zhou, L., & Wang, Y. (2019). Y-SPCR: A new dimensionality reduction method for gene expression data classification. In I. Yoo, J. Bi, & X. T. Hu (Eds.), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 (pp. 401-408). Article 8983403 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM47256.2019.8983403

Li, Jie ; Zhao, Zhun ; Zhou, Li et al. / Y-SPCR : A new dimensionality reduction method for gene expression data classification. Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. editor / Illhoi Yoo ; Jinbo Bi ; Xiaohua Tony Hu. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 401-408 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

@inproceedings{34b2969fd9694668b41bbe918c67f8aa,

title = "Y-SPCR: A new dimensionality reduction method for gene expression data classification",

abstract = "With the increase of the scale and complexity of massive data, data dimensionality reduction technologies, such as principal component analysis, have developed rapidly. The performance of dimension reduction technologies still needs to be further improved. In the paper we proposed a new dimensionality reduction method (Y-SPCR) based Supervised Principal Component Regression (SPCR) and Y-aware Principal Component Regression (Y-aware PCR). Experimental results on four gene expression data sets show that Y-SPCR effectively overcomes the shortcomings of SPCR and Y-aware PCR and improves the accuracy and stability of on gene expression data classification.",

keywords = "SPCR, Y-SPCR, Y-aware PCR, classification, data dimension reduction, gene expression data",

author = "Jie Li and Zhun Zhao and Li Zhou and Yadong Wang",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 ; Conference date: 18-11-2019 Through 21-11-2019",

year = "2019",

month = nov,

doi = "10.1109/BIBM47256.2019.8983403",

language = "英语",

series = "Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019",

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

pages = "401--408",

editor = "Illhoi Yoo and Jinbo Bi and Hu, \{Xiaohua Tony\}",

booktitle = "Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019",

address = "美国",

}

Li, J, Zhao, Z, Zhou, L & Wang, Y 2019, Y-SPCR: A new dimensionality reduction method for gene expression data classification. in I Yoo, J Bi & XT Hu (eds), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019., 8983403, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, Institute of Electrical and Electronics Engineers Inc., pp. 401-408, 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, San Diego, United States, 18/11/19. https://doi.org/10.1109/BIBM47256.2019.8983403

Y-SPCR: A new dimensionality reduction method for gene expression data classification. / Li, Jie; Zhao, Zhun; Zhou, Li et al.
Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. ed. / Illhoi Yoo; Jinbo Bi; Xiaohua Tony Hu. Institute of Electrical and Electronics Engineers Inc., 2019. p. 401-408 8983403 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

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

TY - GEN

T1 - Y-SPCR

T2 - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

AU - Li, Jie

AU - Zhao, Zhun

AU - Zhou, Li

AU - Wang, Yadong

PY - 2019/11

Y1 - 2019/11

N2 - With the increase of the scale and complexity of massive data, data dimensionality reduction technologies, such as principal component analysis, have developed rapidly. The performance of dimension reduction technologies still needs to be further improved. In the paper we proposed a new dimensionality reduction method (Y-SPCR) based Supervised Principal Component Regression (SPCR) and Y-aware Principal Component Regression (Y-aware PCR). Experimental results on four gene expression data sets show that Y-SPCR effectively overcomes the shortcomings of SPCR and Y-aware PCR and improves the accuracy and stability of on gene expression data classification.

AB - With the increase of the scale and complexity of massive data, data dimensionality reduction technologies, such as principal component analysis, have developed rapidly. The performance of dimension reduction technologies still needs to be further improved. In the paper we proposed a new dimensionality reduction method (Y-SPCR) based Supervised Principal Component Regression (SPCR) and Y-aware Principal Component Regression (Y-aware PCR). Experimental results on four gene expression data sets show that Y-SPCR effectively overcomes the shortcomings of SPCR and Y-aware PCR and improves the accuracy and stability of on gene expression data classification.

KW - SPCR

KW - Y-SPCR

KW - Y-aware PCR

KW - classification

KW - data dimension reduction

KW - gene expression data

UR - https://www.scopus.com/pages/publications/85084330476

U2 - 10.1109/BIBM47256.2019.8983403

DO - 10.1109/BIBM47256.2019.8983403

M3 - 会议稿件

AN - SCOPUS:85084330476

T3 - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

SP - 401

EP - 408

BT - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

A2 - Yoo, Illhoi

A2 - Bi, Jinbo

A2 - Hu, Xiaohua Tony

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 18 November 2019 through 21 November 2019

ER -

Li J, Zhao Z, Zhou L, Wang Y. Y-SPCR: A new dimensionality reduction method for gene expression data classification. In Yoo I, Bi J, Hu XT, editors, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 401-408. 8983403. (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). doi: 10.1109/BIBM47256.2019.8983403

Y-SPCR: A new dimensionality reduction method for gene expression data classification

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this