Predictive Meta-analysis of Multiple Microarray Datasets: An Application to Classification of Malignant Gliomas

Nurislam Tursynbek; Ghazal Ghahramany; Sheida Nabavi; Amin Zollanvari

doi:10.1109/BIBM.2018.8621503

Predictive Meta-analysis of Multiple Microarray Datasets: An Application to Classification of Malignant Gliomas

Nurislam Tursynbek, Ghazal Ghahramany, Sheida Nabavi, Amin Zollanvari

Department of Electrical and Computer Engineering

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

Abstract

In this study, we conducted a predictive meta-analysis of multiple microarrays to identify a gene signature that can be potentially used to distinguish different grades of malignant gliomas (i.e., Grade III and IV). We showed our developed classification rule achieved an average accuracy and a J index of 75% and 52.6%, respectively, as measured by a dataset cross-validation strategy. Furthermore, we showed that clustering samples on the basis of similarity of expression profiles of the gene signature divides the data across available studies mainly into the two phenotypic groups regardless of the actual study. From the standpoint of data analytics, the results of this study confirm the utility of meta-analysis in integrating raw data from multiple studies into a predictive framework. From a biological perspective, the identified gene signature can be potentially used to shed light on the molecular mechanisms underlying the formation of malignant gliomas.

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	2423-2428
Number of pages	6
ISBN (Electronic)	9781538654880
DOIs	https://doi.org/10.1109/BIBM.2018.8621503
Publication status	Published - Jan 21 2019
Event	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain Duration: Dec 3 2018 → Dec 6 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	12/3/18 → 12/6/18

Keywords

Elastic Net
Malignant Gliomas
Prediction Analysis of Microarrays
Predictive Meta-analysis
Machine Learning

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics
Artificial Intelligence

Access to Document

10.1109/BIBM.2018.8621503

Cite this

Tursynbek, N., Ghahramany, G., Nabavi, S., & Zollanvari, A. (2019). Predictive Meta-analysis of Multiple Microarray Datasets: An Application to Classification of Malignant Gliomas. 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. 2423-2428). Article 8621503 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2018.8621503

Predictive Meta-analysis of Multiple Microarray Datasets: An Application to Classification of Malignant Gliomas. / Tursynbek, Nurislam; Ghahramany, Ghazal; Nabavi, Sheida 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. 2423-2428 8621503 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

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

Tursynbek, N, Ghahramany, G, Nabavi, S & Zollanvari, A 2019, Predictive Meta-analysis of Multiple Microarray Datasets: An Application to Classification of Malignant Gliomas. 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., 8621503, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Institute of Electrical and Electronics Engineers Inc., pp. 2423-2428, 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Madrid, Spain, 12/3/18. https://doi.org/10.1109/BIBM.2018.8621503

Tursynbek N, Ghahramany G, Nabavi S, Zollanvari A. Predictive Meta-analysis of Multiple Microarray Datasets: An Application to Classification of Malignant Gliomas. 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. 2423-2428. 8621503. (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). doi: 10.1109/BIBM.2018.8621503

Tursynbek, Nurislam ; Ghahramany, Ghazal ; Nabavi, Sheida et al. / Predictive Meta-analysis of Multiple Microarray Datasets : An Application to Classification of Malignant Gliomas. 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. 2423-2428 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

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title = "Predictive Meta-analysis of Multiple Microarray Datasets: An Application to Classification of Malignant Gliomas",

abstract = "In this study, we conducted a predictive meta-analysis of multiple microarrays to identify a gene signature that can be potentially used to distinguish different grades of malignant gliomas (i.e., Grade III and IV). We showed our developed classification rule achieved an average accuracy and a J index of 75% and 52.6%, respectively, as measured by a dataset cross-validation strategy. Furthermore, we showed that clustering samples on the basis of similarity of expression profiles of the gene signature divides the data across available studies mainly into the two phenotypic groups regardless of the actual study. From the standpoint of data analytics, the results of this study confirm the utility of meta-analysis in integrating raw data from multiple studies into a predictive framework. From a biological perspective, the identified gene signature can be potentially used to shed light on the molecular mechanisms underlying the formation of malignant gliomas.",

keywords = "Elastic Net, Malignant Gliomas, Prediction Analysis of Microarrays, Predictive Meta-analysis, Machine Learning",

author = "Nurislam Tursynbek and Ghazal Ghahramany and Sheida Nabavi and Amin Zollanvari",

note = "Funding Information: This work was partially supported by Nazarbayev University Grant SOE2018008. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 ; Conference date: 03-12-2018 Through 06-12-2018",

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AB - In this study, we conducted a predictive meta-analysis of multiple microarrays to identify a gene signature that can be potentially used to distinguish different grades of malignant gliomas (i.e., Grade III and IV). We showed our developed classification rule achieved an average accuracy and a J index of 75% and 52.6%, respectively, as measured by a dataset cross-validation strategy. Furthermore, we showed that clustering samples on the basis of similarity of expression profiles of the gene signature divides the data across available studies mainly into the two phenotypic groups regardless of the actual study. From the standpoint of data analytics, the results of this study confirm the utility of meta-analysis in integrating raw data from multiple studies into a predictive framework. From a biological perspective, the identified gene signature can be potentially used to shed light on the molecular mechanisms underlying the formation of malignant gliomas.

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Predictive Meta-analysis of Multiple Microarray Datasets: An Application to Classification of Malignant Gliomas

Abstract

Publication series

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Keywords

ASJC Scopus subject areas

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