Feature selection of microarray data using multidimensional graph neural network and supernode hierarchical clustering

Abstract Cancer remains a significant cause of mortality, and the application of microarray technology has opened new avenues for cancer diagnosis and treatment. However, due to the challenges in sample acquisition, the genetic dimension of microarray data surpasses the sample dimension, resulting i...
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Autor*in:	Xie, Weidong [verfasserIn] Zhang, Shoujia Wang, Linjie Yu, Kun Li, Wei

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Graph neural networks Feature selection Graph pooling Feature dependencies Microarray data

Anmerkung:	© The Author(s) 2024

Übergeordnetes Werk:	Enthalten in: Artificial intelligence review - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986, 57(2024), 3 vom: 19. Feb.
Übergeordnetes Werk:	volume:57 ; year:2024 ; number:3 ; day:19 ; month:02

Links:	Volltext

DOI / URN:	10.1007/s10462-023-10700-3

Katalog-ID:	SPR054819121

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allfields	10.1007/s10462-023-10700-3 doi (DE-627)SPR054819121 (SPR)s10462-023-10700-3-e DE-627 ger DE-627 rakwb eng Xie, Weidong verfasserin aut Feature selection of microarray data using multidimensional graph neural network and supernode hierarchical clustering 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Cancer remains a significant cause of mortality, and the application of microarray technology has opened new avenues for cancer diagnosis and treatment. However, due to the challenges in sample acquisition, the genetic dimension of microarray data surpasses the sample dimension, resulting in high-dimensional small sample data. Effective feature selection is crucial for identifying biomarkers and facilitating further analysis. However, existing methods struggle to fully exploit the interdependencies among genes, such as regulatory networks and pathways, to guide the feature selection process and construct efficient classification models. In this paper, we propose a novel feature selection algorithm and classification model based on graph neural networks to address these challenges. Our proposed method employs a multidimensional graph to capture intricate gene interactions. We leverage link prediction techniques to enhance the graph structure relationships and employ a multidimensional node evaluator alongside a supernode discovery algorithm based on spectral clustering for initial node filtering. Subsequently, a hierarchical graph pooling technique based on downsampling is used to further refine node selection for feature extraction and model building. We evaluate the proposed method on nine publicly available microarray datasets, and the results demonstrate its superiority over both classical and advanced feature selection techniques in various evaluation metrics. This highlights the effectiveness and advancement of our proposed approach in addressing the complexities associated with microarray data analysis and cancer classification. Graph neural networks (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Graph pooling (dpeaa)DE-He213 Feature dependencies (dpeaa)DE-He213 Microarray data (dpeaa)DE-He213 Zhang, Shoujia aut Wang, Linjie aut Yu, Kun aut Li, Wei aut Enthalten in Artificial intelligence review Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 57(2024), 3 vom: 19. Feb. (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:57 year:2024 number:3 day:19 month:02 https://dx.doi.org/10.1007/s10462-023-10700-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2048 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 57 2024 3 19 02
spelling	10.1007/s10462-023-10700-3 doi (DE-627)SPR054819121 (SPR)s10462-023-10700-3-e DE-627 ger DE-627 rakwb eng Xie, Weidong verfasserin aut Feature selection of microarray data using multidimensional graph neural network and supernode hierarchical clustering 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Cancer remains a significant cause of mortality, and the application of microarray technology has opened new avenues for cancer diagnosis and treatment. However, due to the challenges in sample acquisition, the genetic dimension of microarray data surpasses the sample dimension, resulting in high-dimensional small sample data. Effective feature selection is crucial for identifying biomarkers and facilitating further analysis. However, existing methods struggle to fully exploit the interdependencies among genes, such as regulatory networks and pathways, to guide the feature selection process and construct efficient classification models. In this paper, we propose a novel feature selection algorithm and classification model based on graph neural networks to address these challenges. Our proposed method employs a multidimensional graph to capture intricate gene interactions. We leverage link prediction techniques to enhance the graph structure relationships and employ a multidimensional node evaluator alongside a supernode discovery algorithm based on spectral clustering for initial node filtering. Subsequently, a hierarchical graph pooling technique based on downsampling is used to further refine node selection for feature extraction and model building. We evaluate the proposed method on nine publicly available microarray datasets, and the results demonstrate its superiority over both classical and advanced feature selection techniques in various evaluation metrics. This highlights the effectiveness and advancement of our proposed approach in addressing the complexities associated with microarray data analysis and cancer classification. Graph neural networks (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Graph pooling (dpeaa)DE-He213 Feature dependencies (dpeaa)DE-He213 Microarray data (dpeaa)DE-He213 Zhang, Shoujia aut Wang, Linjie aut Yu, Kun aut Li, Wei aut Enthalten in Artificial intelligence review Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 57(2024), 3 vom: 19. Feb. (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:57 year:2024 number:3 day:19 month:02 https://dx.doi.org/10.1007/s10462-023-10700-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2048 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 57 2024 3 19 02
allfields_unstemmed	10.1007/s10462-023-10700-3 doi (DE-627)SPR054819121 (SPR)s10462-023-10700-3-e DE-627 ger DE-627 rakwb eng Xie, Weidong verfasserin aut Feature selection of microarray data using multidimensional graph neural network and supernode hierarchical clustering 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Cancer remains a significant cause of mortality, and the application of microarray technology has opened new avenues for cancer diagnosis and treatment. However, due to the challenges in sample acquisition, the genetic dimension of microarray data surpasses the sample dimension, resulting in high-dimensional small sample data. Effective feature selection is crucial for identifying biomarkers and facilitating further analysis. However, existing methods struggle to fully exploit the interdependencies among genes, such as regulatory networks and pathways, to guide the feature selection process and construct efficient classification models. In this paper, we propose a novel feature selection algorithm and classification model based on graph neural networks to address these challenges. Our proposed method employs a multidimensional graph to capture intricate gene interactions. We leverage link prediction techniques to enhance the graph structure relationships and employ a multidimensional node evaluator alongside a supernode discovery algorithm based on spectral clustering for initial node filtering. Subsequently, a hierarchical graph pooling technique based on downsampling is used to further refine node selection for feature extraction and model building. We evaluate the proposed method on nine publicly available microarray datasets, and the results demonstrate its superiority over both classical and advanced feature selection techniques in various evaluation metrics. This highlights the effectiveness and advancement of our proposed approach in addressing the complexities associated with microarray data analysis and cancer classification. Graph neural networks (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Graph pooling (dpeaa)DE-He213 Feature dependencies (dpeaa)DE-He213 Microarray data (dpeaa)DE-He213 Zhang, Shoujia aut Wang, Linjie aut Yu, Kun aut Li, Wei aut Enthalten in Artificial intelligence review Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 57(2024), 3 vom: 19. Feb. (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:57 year:2024 number:3 day:19 month:02 https://dx.doi.org/10.1007/s10462-023-10700-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2048 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 57 2024 3 19 02
allfieldsGer	10.1007/s10462-023-10700-3 doi (DE-627)SPR054819121 (SPR)s10462-023-10700-3-e DE-627 ger DE-627 rakwb eng Xie, Weidong verfasserin aut Feature selection of microarray data using multidimensional graph neural network and supernode hierarchical clustering 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Cancer remains a significant cause of mortality, and the application of microarray technology has opened new avenues for cancer diagnosis and treatment. However, due to the challenges in sample acquisition, the genetic dimension of microarray data surpasses the sample dimension, resulting in high-dimensional small sample data. Effective feature selection is crucial for identifying biomarkers and facilitating further analysis. However, existing methods struggle to fully exploit the interdependencies among genes, such as regulatory networks and pathways, to guide the feature selection process and construct efficient classification models. In this paper, we propose a novel feature selection algorithm and classification model based on graph neural networks to address these challenges. Our proposed method employs a multidimensional graph to capture intricate gene interactions. We leverage link prediction techniques to enhance the graph structure relationships and employ a multidimensional node evaluator alongside a supernode discovery algorithm based on spectral clustering for initial node filtering. Subsequently, a hierarchical graph pooling technique based on downsampling is used to further refine node selection for feature extraction and model building. We evaluate the proposed method on nine publicly available microarray datasets, and the results demonstrate its superiority over both classical and advanced feature selection techniques in various evaluation metrics. This highlights the effectiveness and advancement of our proposed approach in addressing the complexities associated with microarray data analysis and cancer classification. Graph neural networks (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Graph pooling (dpeaa)DE-He213 Feature dependencies (dpeaa)DE-He213 Microarray data (dpeaa)DE-He213 Zhang, Shoujia aut Wang, Linjie aut Yu, Kun aut Li, Wei aut Enthalten in Artificial intelligence review Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 57(2024), 3 vom: 19. Feb. (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:57 year:2024 number:3 day:19 month:02 https://dx.doi.org/10.1007/s10462-023-10700-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2048 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 57 2024 3 19 02
allfieldsSound	10.1007/s10462-023-10700-3 doi (DE-627)SPR054819121 (SPR)s10462-023-10700-3-e DE-627 ger DE-627 rakwb eng Xie, Weidong verfasserin aut Feature selection of microarray data using multidimensional graph neural network and supernode hierarchical clustering 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Cancer remains a significant cause of mortality, and the application of microarray technology has opened new avenues for cancer diagnosis and treatment. However, due to the challenges in sample acquisition, the genetic dimension of microarray data surpasses the sample dimension, resulting in high-dimensional small sample data. Effective feature selection is crucial for identifying biomarkers and facilitating further analysis. However, existing methods struggle to fully exploit the interdependencies among genes, such as regulatory networks and pathways, to guide the feature selection process and construct efficient classification models. In this paper, we propose a novel feature selection algorithm and classification model based on graph neural networks to address these challenges. Our proposed method employs a multidimensional graph to capture intricate gene interactions. We leverage link prediction techniques to enhance the graph structure relationships and employ a multidimensional node evaluator alongside a supernode discovery algorithm based on spectral clustering for initial node filtering. Subsequently, a hierarchical graph pooling technique based on downsampling is used to further refine node selection for feature extraction and model building. We evaluate the proposed method on nine publicly available microarray datasets, and the results demonstrate its superiority over both classical and advanced feature selection techniques in various evaluation metrics. This highlights the effectiveness and advancement of our proposed approach in addressing the complexities associated with microarray data analysis and cancer classification. Graph neural networks (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Graph pooling (dpeaa)DE-He213 Feature dependencies (dpeaa)DE-He213 Microarray data (dpeaa)DE-He213 Zhang, Shoujia aut Wang, Linjie aut Yu, Kun aut Li, Wei aut Enthalten in Artificial intelligence review Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 57(2024), 3 vom: 19. Feb. (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:57 year:2024 number:3 day:19 month:02 https://dx.doi.org/10.1007/s10462-023-10700-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2048 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 57 2024 3 19 02
language	English
source	Enthalten in Artificial intelligence review 57(2024), 3 vom: 19. Feb. volume:57 year:2024 number:3 day:19 month:02
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author	Xie, Weidong
spellingShingle	Xie, Weidong misc Graph neural networks misc Feature selection misc Graph pooling misc Feature dependencies misc Microarray data Feature selection of microarray data using multidimensional graph neural network and supernode hierarchical clustering
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topic	misc Graph neural networks misc Feature selection misc Graph pooling misc Feature dependencies misc Microarray data
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title	Feature selection of microarray data using multidimensional graph neural network and supernode hierarchical clustering
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title_sort	feature selection of microarray data using multidimensional graph neural network and supernode hierarchical clustering
title_auth	Feature selection of microarray data using multidimensional graph neural network and supernode hierarchical clustering
abstract	Abstract Cancer remains a significant cause of mortality, and the application of microarray technology has opened new avenues for cancer diagnosis and treatment. However, due to the challenges in sample acquisition, the genetic dimension of microarray data surpasses the sample dimension, resulting in high-dimensional small sample data. Effective feature selection is crucial for identifying biomarkers and facilitating further analysis. However, existing methods struggle to fully exploit the interdependencies among genes, such as regulatory networks and pathways, to guide the feature selection process and construct efficient classification models. In this paper, we propose a novel feature selection algorithm and classification model based on graph neural networks to address these challenges. Our proposed method employs a multidimensional graph to capture intricate gene interactions. We leverage link prediction techniques to enhance the graph structure relationships and employ a multidimensional node evaluator alongside a supernode discovery algorithm based on spectral clustering for initial node filtering. Subsequently, a hierarchical graph pooling technique based on downsampling is used to further refine node selection for feature extraction and model building. We evaluate the proposed method on nine publicly available microarray datasets, and the results demonstrate its superiority over both classical and advanced feature selection techniques in various evaluation metrics. This highlights the effectiveness and advancement of our proposed approach in addressing the complexities associated with microarray data analysis and cancer classification. © The Author(s) 2024
abstractGer	Abstract Cancer remains a significant cause of mortality, and the application of microarray technology has opened new avenues for cancer diagnosis and treatment. However, due to the challenges in sample acquisition, the genetic dimension of microarray data surpasses the sample dimension, resulting in high-dimensional small sample data. Effective feature selection is crucial for identifying biomarkers and facilitating further analysis. However, existing methods struggle to fully exploit the interdependencies among genes, such as regulatory networks and pathways, to guide the feature selection process and construct efficient classification models. In this paper, we propose a novel feature selection algorithm and classification model based on graph neural networks to address these challenges. Our proposed method employs a multidimensional graph to capture intricate gene interactions. We leverage link prediction techniques to enhance the graph structure relationships and employ a multidimensional node evaluator alongside a supernode discovery algorithm based on spectral clustering for initial node filtering. Subsequently, a hierarchical graph pooling technique based on downsampling is used to further refine node selection for feature extraction and model building. We evaluate the proposed method on nine publicly available microarray datasets, and the results demonstrate its superiority over both classical and advanced feature selection techniques in various evaluation metrics. This highlights the effectiveness and advancement of our proposed approach in addressing the complexities associated with microarray data analysis and cancer classification. © The Author(s) 2024
abstract_unstemmed	Abstract Cancer remains a significant cause of mortality, and the application of microarray technology has opened new avenues for cancer diagnosis and treatment. However, due to the challenges in sample acquisition, the genetic dimension of microarray data surpasses the sample dimension, resulting in high-dimensional small sample data. Effective feature selection is crucial for identifying biomarkers and facilitating further analysis. However, existing methods struggle to fully exploit the interdependencies among genes, such as regulatory networks and pathways, to guide the feature selection process and construct efficient classification models. In this paper, we propose a novel feature selection algorithm and classification model based on graph neural networks to address these challenges. Our proposed method employs a multidimensional graph to capture intricate gene interactions. We leverage link prediction techniques to enhance the graph structure relationships and employ a multidimensional node evaluator alongside a supernode discovery algorithm based on spectral clustering for initial node filtering. Subsequently, a hierarchical graph pooling technique based on downsampling is used to further refine node selection for feature extraction and model building. We evaluate the proposed method on nine publicly available microarray datasets, and the results demonstrate its superiority over both classical and advanced feature selection techniques in various evaluation metrics. This highlights the effectiveness and advancement of our proposed approach in addressing the complexities associated with microarray data analysis and cancer classification. © The Author(s) 2024
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title_short	Feature selection of microarray data using multidimensional graph neural network and supernode hierarchical clustering
url	https://dx.doi.org/10.1007/s10462-023-10700-3
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However, due to the challenges in sample acquisition, the genetic dimension of microarray data surpasses the sample dimension, resulting in high-dimensional small sample data. Effective feature selection is crucial for identifying biomarkers and facilitating further analysis. However, existing methods struggle to fully exploit the interdependencies among genes, such as regulatory networks and pathways, to guide the feature selection process and construct efficient classification models. In this paper, we propose a novel feature selection algorithm and classification model based on graph neural networks to address these challenges. Our proposed method employs a multidimensional graph to capture intricate gene interactions. We leverage link prediction techniques to enhance the graph structure relationships and employ a multidimensional node evaluator alongside a supernode discovery algorithm based on spectral clustering for initial node filtering. Subsequently, a hierarchical graph pooling technique based on downsampling is used to further refine node selection for feature extraction and model building. We evaluate the proposed method on nine publicly available microarray datasets, and the results demonstrate its superiority over both classical and advanced feature selection techniques in various evaluation metrics. This highlights the effectiveness and advancement of our proposed approach in addressing the complexities associated with microarray data analysis and cancer classification.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Graph neural networks</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature selection</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Graph pooling</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature dependencies</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Microarray data</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Shoujia</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Linjie</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yu, Kun</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Wei</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Artificial intelligence review</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986</subfield><subfield code="g">57(2024), 3 vom: 19. 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Feature selection of microarray data using multidimensional graph neural network and supernode hierarchical clustering

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