Journal of
Systemics, Cybernetics and Informatics
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ISSN: 1690-4524 (Online)


Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.

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Published by
The International Institute of Informatics and Cybernetics


Re-Published in
Academia.edu
(A Community of about 40.000.000 Academics)


Honorary Editorial Advisory Board's Chair
William Lesso (1931-2015)

Editor-in-Chief
Nagib C. Callaos


Sponsored by
The International Institute of
Informatics and Systemics

www.iiis.org
 

Editorial Advisory Board

Quality Assurance

Editors

Journal's Reviewers
Call for Special Articles
 

Description and Aims

Submission of Articles

Areas and Subareas

Information to Contributors

Editorial Peer Review Methodology

Integrating Reviewing Processes


Education 5.0: Using the Design Thinking Process – An Interdisciplinary View
Birgit Oberer, Alptekin Erkollar
(pages: 1-17)

Impact of Artificial Intelligence on Smart Cities
Mohammad Ilyas
(pages: 18-39)

A Multi-Disciplinary Cybernetic Approach to Pedagogic Excellence
Russell Jay Hendel
(pages: 40-63)

Data Management Sharing Plan: Fostering Effective Trans-Disciplinary Communication in Collaborative Research
Cristo Ernesto Yáńez León, James Lipuma
(pages: 64-79)

From Disunity to Synergy: Transdisciplinarity in HR Trends
Olga Bernikova, Daria Frolova
(pages: 80-92)

The Impact of Artificial Intelligence on the Future Business World
Hebah Y. AlQato
(pages: 93-104)

Wi-Fi and the Wisdom Exchange: The Role of Lived Experience in the Age of AI
Teresa H. Langness
(pages: 105-113)

Older Adult Online Learning during COVID-19 in Taiwan: Based on Teachers' Perspective
Ya-Hui Lee, Yi-Fen Wang, Hsien-Ta Cha
(pages: 114-129)

Data Visualization of Budgeting Assumptions: An Illustrative Case of Trans-disciplinary Applied Knowledge
Carol E. Cuthbert, Noel J. Pears, Karen Bradshaw
(pages: 130-149)

The Importance of Defining Cybersecurity from a Transdisciplinary Approach
Bilquis Ferdousi
(pages: 150-164)

ChatGPT, Metaverses and the Future of Transdisciplinary Communication
Jasmin (Bey) Cowin
(pages: 165-178)

Trans-Disciplinary Communication for Policy Making: A Reflective Activity Study
Cristo Leon
(pages: 179-192)

Trans-Disciplinary Communication in Collaborative Co-Design for Knowledge Sharing
James Lipuma, Cristo Leon
(pages: 193-210)

Digital Games in Education: An Interdisciplinary View
Birgit Oberer, Alptekin Erkollar
(pages: 211-230)

Disciplinary Inbreeding or Disciplinary Integration?
Nagib Callaos
(pages: 231-281)


 

Abstracts

 


ABSTRACT


GEGEINTOOL: A Computer-Based Tool for Automated Analysis of Gene-Gene Interactions in Large Epidemiological Studies in Cardiovascular Genomics

Oscar Coltell, José M. Ordovás, Carmen Saiz, Manuel Forner, Francisco Gabriel, Dolores Corella


Current methods of data analysis of gene-gene interactions in complex diseases, after taking into account environmental factors using traditional approaches, are inefficient. High-throughput methods of analysis in large scale studies including thousands of subjects and hundreds of SNPs should be implemented. We developed an integrative computer tool, GEGEINTOOL (GEne- GEne INTeraction tOOL), for large-scale analysis of gene-gene interactions, in human studies of complex diseases including a large number of subjects, SNPs, as well as environmental factors. That resource uses standard statistical packages (SPSS, etc.) to build and fit the gene-gene interaction models by means of syntax scripts in predicting one or more continuous or dichotomic phenotypes. Codominant, dominant and recessive genetic interaction models including control for covariates are automatically created for each SNP in order to test the best model. From the standard outputs, GEGEINTOOL extracts a selected set of parameters (regression coefficients, p-values, adjusted means, etc.), and groups them in a single MS Excel Spreadsheet. The tool allows editing the set of filter parameters, filtering the selected results depending on p-values, as well as plotting the selected gene-gene interactions to check consistency. In conclusion, GEGEINTOOL is a useful and friendly tool for exploring and identifying gene-gene interactions in complex diseases.

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