A Sparse Multi-task Contrastive and Discriminative Learning Method with Feature Selection for Brain Imaging Genetics

Jin Zhang; Muheng Shang; Qiang Xie; Minjianan Zhang; Duo Xi; Lei Guo; Junwei Han; Lei Du

doi:10.1109/BIBM55620.2022.9995050

A Sparse Multi-task Contrastive and Discriminative Learning Method with Feature Selection for Brain Imaging Genetics

Jin Zhang
, Muheng Shang
, Qiang Xie
, Minjianan Zhang
, Duo Xi
, Lei Guo
, Junwei Han
, Lei Du

School of Automation

Northwestern Polytechnical University Xian

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

4 Scopus citations

Abstract

Alzheimer's disease (AD) is a very complex neurodegenerative disease. Generally, different diagnostic groups could exhibit discriminative and specific patterns, including the single nucleotide polymorphisms (SNPs), brain imaging quantitative traits (QTs), as well as their associations, which may facilitate the comprehensive understanding of AD. However, most existing methods cannot guarantee to identify discriminative or class-specific biomarkers or both of them. To overcome this shortcoming, we propose a sparse multi-task contrastive and discriminative learning approach (MTCDA) to jointly learn the discriminative and specific patterns for multiple diagnostic groups. MTCDA can identify the class-relevant and discriminative SNP-QTs associations, and relevant SNPs, imaging QTs underpinning this relationship. We introduce an efficient algorithm to solve the proposed method which converges to a local optimum. The experimental results on Alzheimer's Disease Neuroimaging Initiative (ADNI) show that MTCDA can obtain higher canonical correlation coefficients, classification accuracy and better feature selection results than state-of-the-art methods, which demonstrates the potential of our method for multi-class brain imaging genetics.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
Editors	Donald Adjeroh, Qi Long, Xinghua Shi, Fei Guo, Xiaohua Hu, Srinivas Aluru, Giri Narasimhan, Jianxin Wang, Mingon Kang, Ananda M. Mondal, Jin Liu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	660-665
Number of pages	6
ISBN (Electronic)	9781665468190
DOIs	https://doi.org/10.1109/BIBM55620.2022.9995050
State	Published - 2022
Event	2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 - Las Vegas, United States Duration: 6 Dec 2022 → 8 Dec 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

Conference

Conference	2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
Country/Territory	United States
City	Las Vegas
Period	6/12/22 → 8/12/22

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Brain imaging genetics
contrastive learning
feature selection
multi-task learning

Access to Document

10.1109/BIBM55620.2022.9995050

Cite this

Zhang, J., Shang, M., Xie, Q., Zhang, M., Xi, D., Guo, L., Han, J., & Du, L. (2022). A Sparse Multi-task Contrastive and Discriminative Learning Method with Feature Selection for Brain Imaging Genetics. In D. Adjeroh, Q. Long, X. Shi, F. Guo, X. Hu, S. Aluru, G. Narasimhan, J. Wang, M. Kang, A. M. Mondal, & J. Liu (Eds.), Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 (pp. 660-665). (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM55620.2022.9995050

Zhang, Jin ; Shang, Muheng ; Xie, Qiang et al. / A Sparse Multi-task Contrastive and Discriminative Learning Method with Feature Selection for Brain Imaging Genetics. Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. editor / Donald Adjeroh ; Qi Long ; Xinghua Shi ; Fei Guo ; Xiaohua Hu ; Srinivas Aluru ; Giri Narasimhan ; Jianxin Wang ; Mingon Kang ; Ananda M. Mondal ; Jin Liu. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 660-665 (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022).

@inproceedings{d3e276f2966f47ddb3c33f64ace8ecc0,

title = "A Sparse Multi-task Contrastive and Discriminative Learning Method with Feature Selection for Brain Imaging Genetics",

abstract = "Alzheimer's disease (AD) is a very complex neurodegenerative disease. Generally, different diagnostic groups could exhibit discriminative and specific patterns, including the single nucleotide polymorphisms (SNPs), brain imaging quantitative traits (QTs), as well as their associations, which may facilitate the comprehensive understanding of AD. However, most existing methods cannot guarantee to identify discriminative or class-specific biomarkers or both of them. To overcome this shortcoming, we propose a sparse multi-task contrastive and discriminative learning approach (MTCDA) to jointly learn the discriminative and specific patterns for multiple diagnostic groups. MTCDA can identify the class-relevant and discriminative SNP-QTs associations, and relevant SNPs, imaging QTs underpinning this relationship. We introduce an efficient algorithm to solve the proposed method which converges to a local optimum. The experimental results on Alzheimer's Disease Neuroimaging Initiative (ADNI) show that MTCDA can obtain higher canonical correlation coefficients, classification accuracy and better feature selection results than state-of-the-art methods, which demonstrates the potential of our method for multi-class brain imaging genetics.",

keywords = "Brain imaging genetics, contrastive learning, feature selection, multi-task learning",

author = "Jin Zhang and Muheng Shang and Qiang Xie and Minjianan Zhang and Duo Xi and Lei Guo and Junwei Han and Lei Du",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 ; Conference date: 06-12-2022 Through 08-12-2022",

year = "2022",

doi = "10.1109/BIBM55620.2022.9995050",

language = "英语",

series = "Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022",

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

pages = "660--665",

editor = "Donald Adjeroh and Qi Long and Xinghua Shi and Fei Guo and Xiaohua Hu and Srinivas Aluru and Giri Narasimhan and Jianxin Wang and Mingon Kang and Mondal, \{Ananda M.\} and Jin Liu",

booktitle = "Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022",

}

Zhang, J, Shang, M, Xie, Q, Zhang, M, Xi, D, Guo, L , Han, J & Du, L 2022, A Sparse Multi-task Contrastive and Discriminative Learning Method with Feature Selection for Brain Imaging Genetics. in D Adjeroh, Q Long, X Shi, F Guo, X Hu, S Aluru, G Narasimhan, J Wang, M Kang, AM Mondal & J Liu (eds), Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022, Institute of Electrical and Electronics Engineers Inc., pp. 660-665, 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022, Las Vegas, United States, 6/12/22. https://doi.org/10.1109/BIBM55620.2022.9995050

A Sparse Multi-task Contrastive and Discriminative Learning Method with Feature Selection for Brain Imaging Genetics. / Zhang, Jin; Shang, Muheng; Xie, Qiang et al.
Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. ed. / Donald Adjeroh; Qi Long; Xinghua Shi; Fei Guo; Xiaohua Hu; Srinivas Aluru; Giri Narasimhan; Jianxin Wang; Mingon Kang; Ananda M. Mondal; Jin Liu. Institute of Electrical and Electronics Engineers Inc., 2022. p. 660-665 (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022).

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

TY - GEN

T1 - A Sparse Multi-task Contrastive and Discriminative Learning Method with Feature Selection for Brain Imaging Genetics

AU - Zhang, Jin

AU - Shang, Muheng

AU - Xie, Qiang

AU - Zhang, Minjianan

AU - Xi, Duo

AU - Guo, Lei

AU - Han, Junwei

AU - Du, Lei

PY - 2022

Y1 - 2022

N2 - Alzheimer's disease (AD) is a very complex neurodegenerative disease. Generally, different diagnostic groups could exhibit discriminative and specific patterns, including the single nucleotide polymorphisms (SNPs), brain imaging quantitative traits (QTs), as well as their associations, which may facilitate the comprehensive understanding of AD. However, most existing methods cannot guarantee to identify discriminative or class-specific biomarkers or both of them. To overcome this shortcoming, we propose a sparse multi-task contrastive and discriminative learning approach (MTCDA) to jointly learn the discriminative and specific patterns for multiple diagnostic groups. MTCDA can identify the class-relevant and discriminative SNP-QTs associations, and relevant SNPs, imaging QTs underpinning this relationship. We introduce an efficient algorithm to solve the proposed method which converges to a local optimum. The experimental results on Alzheimer's Disease Neuroimaging Initiative (ADNI) show that MTCDA can obtain higher canonical correlation coefficients, classification accuracy and better feature selection results than state-of-the-art methods, which demonstrates the potential of our method for multi-class brain imaging genetics.

AB - Alzheimer's disease (AD) is a very complex neurodegenerative disease. Generally, different diagnostic groups could exhibit discriminative and specific patterns, including the single nucleotide polymorphisms (SNPs), brain imaging quantitative traits (QTs), as well as their associations, which may facilitate the comprehensive understanding of AD. However, most existing methods cannot guarantee to identify discriminative or class-specific biomarkers or both of them. To overcome this shortcoming, we propose a sparse multi-task contrastive and discriminative learning approach (MTCDA) to jointly learn the discriminative and specific patterns for multiple diagnostic groups. MTCDA can identify the class-relevant and discriminative SNP-QTs associations, and relevant SNPs, imaging QTs underpinning this relationship. We introduce an efficient algorithm to solve the proposed method which converges to a local optimum. The experimental results on Alzheimer's Disease Neuroimaging Initiative (ADNI) show that MTCDA can obtain higher canonical correlation coefficients, classification accuracy and better feature selection results than state-of-the-art methods, which demonstrates the potential of our method for multi-class brain imaging genetics.

KW - Brain imaging genetics

KW - contrastive learning

KW - feature selection

KW - multi-task learning

UR - https://www.scopus.com/pages/publications/85146637674

U2 - 10.1109/BIBM55620.2022.9995050

DO - 10.1109/BIBM55620.2022.9995050

M3 - 会议稿件

AN - SCOPUS:85146637674

T3 - Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

SP - 660

EP - 665

BT - Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

A2 - Adjeroh, Donald

A2 - Long, Qi

A2 - Shi, Xinghua

A2 - Guo, Fei

A2 - Hu, Xiaohua

A2 - Aluru, Srinivas

A2 - Narasimhan, Giri

A2 - Wang, Jianxin

A2 - Kang, Mingon

A2 - Mondal, Ananda M.

A2 - Liu, Jin

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

Y2 - 6 December 2022 through 8 December 2022

ER -

Zhang J, Shang M, Xie Q, Zhang M, Xi D, Guo L et al. A Sparse Multi-task Contrastive and Discriminative Learning Method with Feature Selection for Brain Imaging Genetics. In Adjeroh D, Long Q, Shi X, Guo F, Hu X, Aluru S, Narasimhan G, Wang J, Kang M, Mondal AM, Liu J, editors, Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 660-665. (Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022). doi: 10.1109/BIBM55620.2022.9995050

A Sparse Multi-task Contrastive and Discriminative Learning Method with Feature Selection for Brain Imaging Genetics

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UN SDGs

Keywords

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