Analyzing the Mutation Frequencies and Correlation of Genetic Diseases in Worldwide Populations Using Big Data Processing, Clustering, and Predictive Analytics

Kae Sawada; Michael W. Clark; Nabil Alshurafa; Mohammad Pourhomayoun

doi:10.1109/CSCI.2017.255

Analyzing the Mutation Frequencies and Correlation of Genetic Diseases in Worldwide Populations Using Big Data Processing, Clustering, and Predictive Analytics

Kae Sawada, Michael W. Clark, Nabil Alshurafa, Mohammad Pourhomayoun

Preventive Medicine

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

Abstract

In this paper, we utilize Big Data Processing and develop Predictive Analytics Models to examine and analyze mutations associated with osteoporosis and cardiovascular disease. The dataset consists of the genomic information of over 2,500 individuals. The genomic data was collected from all around the world. The data visualization allowed us to see geographical/regional clustering patterns in the above mentioned specific mutations. The visualized data clearly shows a high correlation between a person's regional background and the occurrence of the 35 single nucleotide polymorphisms (SNPs). The 35 SNPs are specifically associated with osteoporosis and/or cardiovascular disease (CVD). A predictive analytics model was developed based on machine learning algorithms to predict the risk of an individual manifesting osteoporosis in later life. The results of this predictive model confirmed the links between osteoporosis and Cardiovascular related parameters such as High Density Lipoprotein (HDL) and Systolic Blood Pressure (SBP), as determined by the preceding studies.

Original language	English (US)
Title of host publication	Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017
Editors	Fernando G. Tinetti, Quoc-Nam Tran, Leonidas Deligiannidis, Mary Qu Yang, Mary Qu Yang, Hamid R. Arabnia
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1459-1464
Number of pages	6
ISBN (Electronic)	9781538626528
DOIs	https://doi.org/10.1109/CSCI.2017.255
State	Published - Dec 4 2018
Event	2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017 - Las Vegas, United States Duration: Dec 14 2017 → Dec 16 2017

Publication series

Name	Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017

Other

Other	2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017
Country/Territory	United States
City	Las Vegas
Period	12/14/17 → 12/16/17

Keywords

1000 Genome Project
Classifiers
Clustering
Data Visualization
Genome Wide Association Study (GWAS)
Machine Learning
Predictive Model
Supervised Learning
osteoporosis

ASJC Scopus subject areas

Computer Science (miscellaneous)
Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Safety, Risk, Reliability and Quality

UN SDGs

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

Access to Document

10.1109/CSCI.2017.255

Cite this

Sawada, K., Clark, M. W., Alshurafa, N., & Pourhomayoun, M. (2018). Analyzing the Mutation Frequencies and Correlation of Genetic Diseases in Worldwide Populations Using Big Data Processing, Clustering, and Predictive Analytics. In F. G. Tinetti, Q.-N. Tran, L. Deligiannidis, M. Q. Yang, M. Q. Yang, & H. R. Arabnia (Eds.), Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017 (pp. 1459-1464). Article 8561018 (Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSCI.2017.255

Sawada, Kae ; Clark, Michael W. ; Alshurafa, Nabil et al. / Analyzing the Mutation Frequencies and Correlation of Genetic Diseases in Worldwide Populations Using Big Data Processing, Clustering, and Predictive Analytics. Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017. editor / Fernando G. Tinetti ; Quoc-Nam Tran ; Leonidas Deligiannidis ; Mary Qu Yang ; Mary Qu Yang ; Hamid R. Arabnia. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1459-1464 (Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017).

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title = "Analyzing the Mutation Frequencies and Correlation of Genetic Diseases in Worldwide Populations Using Big Data Processing, Clustering, and Predictive Analytics",

abstract = "In this paper, we utilize Big Data Processing and develop Predictive Analytics Models to examine and analyze mutations associated with osteoporosis and cardiovascular disease. The dataset consists of the genomic information of over 2,500 individuals. The genomic data was collected from all around the world. The data visualization allowed us to see geographical/regional clustering patterns in the above mentioned specific mutations. The visualized data clearly shows a high correlation between a person's regional background and the occurrence of the 35 single nucleotide polymorphisms (SNPs). The 35 SNPs are specifically associated with osteoporosis and/or cardiovascular disease (CVD). A predictive analytics model was developed based on machine learning algorithms to predict the risk of an individual manifesting osteoporosis in later life. The results of this predictive model confirmed the links between osteoporosis and Cardiovascular related parameters such as High Density Lipoprotein (HDL) and Systolic Blood Pressure (SBP), as determined by the preceding studies.",

keywords = "1000 Genome Project, Classifiers, Clustering, Data Visualization, Genome Wide Association Study (GWAS), Machine Learning, Predictive Model, Supervised Learning, osteoporosis",

author = "Kae Sawada and Clark, {Michael W.} and Nabil Alshurafa and Mohammad Pourhomayoun",

note = "Funding Information: The authors wish to thank Drs. Rashmi Shah, Bradley Clement, Jose Macias, Daniel H. Sohn, and Dan E. Karlsson with NASA Jet Propulsion Laboratory (JPL) and California State University of Los Angeles (CSULA). This manuscript was enhanced by Rashmi Shah with New Researchers' Support Group at JPL. This work was supported in part by Office of Graduate Studies at CSULA. Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017 ; Conference date: 14-12-2017 Through 16-12-2017",

year = "2018",

month = dec,

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doi = "10.1109/CSCI.2017.255",

language = "English (US)",

series = "Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017",

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

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Sawada, K, Clark, MW, Alshurafa, N & Pourhomayoun, M 2018, Analyzing the Mutation Frequencies and Correlation of Genetic Diseases in Worldwide Populations Using Big Data Processing, Clustering, and Predictive Analytics. in FG Tinetti, Q-N Tran, L Deligiannidis, MQ Yang, MQ Yang & HR Arabnia (eds), Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017., 8561018, Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017, Institute of Electrical and Electronics Engineers Inc., pp. 1459-1464, 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017, Las Vegas, United States, 12/14/17. https://doi.org/10.1109/CSCI.2017.255

Analyzing the Mutation Frequencies and Correlation of Genetic Diseases in Worldwide Populations Using Big Data Processing, Clustering, and Predictive Analytics. / Sawada, Kae; Clark, Michael W.; Alshurafa, Nabil et al.
Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017. ed. / Fernando G. Tinetti; Quoc-Nam Tran; Leonidas Deligiannidis; Mary Qu Yang; Mary Qu Yang; Hamid R. Arabnia. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1459-1464 8561018 (Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017).

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

TY - GEN

T1 - Analyzing the Mutation Frequencies and Correlation of Genetic Diseases in Worldwide Populations Using Big Data Processing, Clustering, and Predictive Analytics

AU - Sawada, Kae

AU - Clark, Michael W.

AU - Alshurafa, Nabil

AU - Pourhomayoun, Mohammad

N1 - Funding Information: The authors wish to thank Drs. Rashmi Shah, Bradley Clement, Jose Macias, Daniel H. Sohn, and Dan E. Karlsson with NASA Jet Propulsion Laboratory (JPL) and California State University of Los Angeles (CSULA). This manuscript was enhanced by Rashmi Shah with New Researchers' Support Group at JPL. This work was supported in part by Office of Graduate Studies at CSULA. Publisher Copyright: © 2017 IEEE.

PY - 2018/12/4

Y1 - 2018/12/4

N2 - In this paper, we utilize Big Data Processing and develop Predictive Analytics Models to examine and analyze mutations associated with osteoporosis and cardiovascular disease. The dataset consists of the genomic information of over 2,500 individuals. The genomic data was collected from all around the world. The data visualization allowed us to see geographical/regional clustering patterns in the above mentioned specific mutations. The visualized data clearly shows a high correlation between a person's regional background and the occurrence of the 35 single nucleotide polymorphisms (SNPs). The 35 SNPs are specifically associated with osteoporosis and/or cardiovascular disease (CVD). A predictive analytics model was developed based on machine learning algorithms to predict the risk of an individual manifesting osteoporosis in later life. The results of this predictive model confirmed the links between osteoporosis and Cardiovascular related parameters such as High Density Lipoprotein (HDL) and Systolic Blood Pressure (SBP), as determined by the preceding studies.

AB - In this paper, we utilize Big Data Processing and develop Predictive Analytics Models to examine and analyze mutations associated with osteoporosis and cardiovascular disease. The dataset consists of the genomic information of over 2,500 individuals. The genomic data was collected from all around the world. The data visualization allowed us to see geographical/regional clustering patterns in the above mentioned specific mutations. The visualized data clearly shows a high correlation between a person's regional background and the occurrence of the 35 single nucleotide polymorphisms (SNPs). The 35 SNPs are specifically associated with osteoporosis and/or cardiovascular disease (CVD). A predictive analytics model was developed based on machine learning algorithms to predict the risk of an individual manifesting osteoporosis in later life. The results of this predictive model confirmed the links between osteoporosis and Cardiovascular related parameters such as High Density Lipoprotein (HDL) and Systolic Blood Pressure (SBP), as determined by the preceding studies.

KW - 1000 Genome Project

KW - Classifiers

KW - Clustering

KW - Data Visualization

KW - Genome Wide Association Study (GWAS)

KW - Machine Learning

KW - Predictive Model

KW - Supervised Learning

KW - osteoporosis

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DO - 10.1109/CSCI.2017.255

M3 - Conference contribution

AN - SCOPUS:85060653936

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BT - Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017

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A2 - Arabnia, Hamid R.

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Sawada K, Clark MW, Alshurafa N, Pourhomayoun M. Analyzing the Mutation Frequencies and Correlation of Genetic Diseases in Worldwide Populations Using Big Data Processing, Clustering, and Predictive Analytics. In Tinetti FG, Tran QN, Deligiannidis L, Yang MQ, Yang MQ, Arabnia HR, editors, Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1459-1464. 8561018. (Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017). doi: 10.1109/CSCI.2017.255

Analyzing the Mutation Frequencies and Correlation of Genetic Diseases in Worldwide Populations Using Big Data Processing, Clustering, and Predictive Analytics

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

UN SDGs

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