research-article

Machine Learning Approaches to Investigate the Relationship between Genetic Factors and Autism Spectrum Disorder

Authors:

Omar DibAuthors Info & Claims

MLMI '21: Proceedings of the 2021 4th International Conference on Machine Learning and Machine Intelligence

Pages 164 - 171

https://doi.org/10.1145/3490725.3490750

Published: 29 December 2021 Publication History

First page of PDF

Supplementary Material

Presentation slides. This research was in part funded by the grant from the Ministry of Education and Science of the Republic of Kazakhstan number AP05135184. Thanks to Akbota Kanderzhanova from School of Medicine, Nazarbayev University, Nursultan, Kazakhstan for data collection and making this research possible. (PresentationMLMI.pptx)

Download
1.51 MB

References

[1]

L. Rylaarsdam and A. Guemez-Gamboa, "Genetic Causes and Modifiers of Autism Spectrum Disorder", Frontiers in Cellular Neuroscience, vol. 13, 2019.

[2]

M. Hassan and H. Mokhtar, "Investigating autism etiology and heterogeneity by decision tree algorithm", Informatics in Medicine Unlocked, vol. 16, p. 100215, 2019.

[3]

S. Spence, "The genetics of autism", Seminars in Pediatric Neurology, vol. 11, no. 3, pp. 196-204, 2004.

[4]

L. Emberti Gialloreti and P. Curatolo, "Autism Spectrum Disorder: Why Do We Know So Little?", Frontiers in Neurology, vol. 9, 2018.

[5]

P. Chaste and M. Leboyer, "Autism risk factors: genes, environment, and gene-environment interactions", Autism and Related Developmental Disorders, vol. 14, no. 3, pp. 281-292, 2012.

[6]

G. Bjørklund, M. Waly, Y. Al-Farsi, K. Saad, M. Dadar, M. Rahman, A. Elhoufey, S. Chirumbolo, J. Jóźwik-Pruska and J. Kałużna-Czaplińska, "The Role of Vitamins in Autism Spectrum Disorder: What Do We Know?", Journal of Molecular Neuroscience, vol. 67, no. 3, pp. 373-387, 2019.

[7]

C. Angermueller, T. Pärnamaa, L. Parts and O. Stegle, "Deep learning for computational biology", Molecular Systems Biology, vol. 12, no. 7, p. 878, 2016.

[8]

D. Howsmon, U. Kruger, S. Melnyk, S. James and J. Hahn, "Classification and adaptive behavior prediction of children with autism spectrum disorder based upon multivariate data analysis of markers of oxidative stress and DNA methylation", PLOS Computational Biology, vol. 13, no. 3, p. e1005385, 2017.

[9]

Y. Li, F. Wu and A. Ngom, "A review on machine learning principles for multi-view biological data integration", Briefings in Bioinformatics, p. bbw113, 2016.

[10]

I. Jolliffe and J. Cadima, "Principal component analysis: a review and recent developments", Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 374, no. 2065, p. 20150202, 2016.

[11]

F. Martinez-Murcia, M. Lai, J. Górriz, J. Ramírez, A. Young, S. Deoni, C. Ecker, M. Lombardo, S. Baron-Cohen, D. Murphy, E. Bullmore and J. Suckling, "On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance-weighted principal component analysis", Human Brain Mapping, vol. 38, no. 3, pp. 1208-1223, 2016.

[12]

M. Asif, H. Martiniano, A. Vicente and F. Couto, "Identifying disease genes using machine learning and gene functional similarities, assessed through Gene Ontology", PLOS ONE, vol. 13, no. 12, p. e0208626, 2018.

[13]

R. Olson, N. Bartley, R. Urbanowicz and J. Moore, "Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science", Proceedings of the Genetic and Evolutionary Computation Conference 2016, 2016.

Digital Library

[14]

R. Olson, R. Urbanowicz, P. Andrews, N. Lavender, L. Kidd and J. Moore, "Automating Biomedical Data Science Through Tree-Based Pipeline Optimization", Applications of Evolutionary Computation, pp. 123-137, 2016.

[15]

T. Le, W. Fu and J. Moore, "Scaling tree-based automated machine learning to biomedical big data with a feature set selector", Bioinformatics, vol. 36, no. 1, pp. 250-256, 2019.

[16]

D. Savic, G. Bell and M. Nobrega, "An in vivo cis-Regulatory Screen at the Type 2 Diabetes Associated TCF7L2 Locus Identifies Multiple Tissue-Specific Enhancers", PLoS ONE, vol. 7, no. 5, p. e36501, 2012. Available: 10.1371/journal.pone.0036501 [Accessed 20 August 2021].

[17]

S. Kumar, K. Reynolds, Y. Ji, R. Gu, S. Rai and C. Zhou, "Impaired neurodevelopmental pathways in autism spectrum disorder: a review of signaling mechanisms and crosstalk", Journal of Neurodevelopmental Disorders, vol. 11, no. 1, 2019. Available: 10.1186/s11689-019-9268-y [Accessed 20 August 2021].

[18]

C. Chan, K. Mukhtarova, A. Kanderzhanova and A. Issanov, "Genetic Variations Influencing Glucose Homeostasis and Insulin Secretion and their Associations with Autism Spectrum Disorder in Kazakhstan", Electronic Journal of General Medicine, vol. 18, no. 2, p. em274, 2021. Available: 10.29333/ejgm/9677 [Accessed 20 August 2021].

[19]

H. Ross, Y. Guo, K. Coleman, O. Ousley and A. Miller, "Association of IL-12p70 and IL-6:IL-10 ratio with autism-related behaviors in 22q11.2 deletion syndrome: A preliminary report", Brain, Behavior, and Immunity, vol. 31, pp. 76-81, 2013. Available: 10.1016/j.bbi.2012.12.021 [Accessed 20 August 2021].

[20]

A. Saghazadeh, B. Ataeinia, K. Keynejad, A. Abdolalizadeh, A. Hirbod-Mobarakeh and N. Rezaei, "Anti-inflammatory cytokines in autism spectrum disorders: A systematic review and meta-analysis", Cytokine, vol. 123, p. 154740, 2019. Available: 10.1016/j.cyto.2019.154740 [Accessed 20 August 2021].

[21]

L. Zuo, "Long noncoding RNAs in psychiatric disorders", Psychiatric Genetics, vol. 26, no. 3, pp. 109-116, 2016. Available: 10.1097/ypg.0000000000000129 [Accessed 20 August 2021].

[22]

E. Oleson, L. Hamilton and D. Gomez, "Cannabinoid Modulation of Dopamine Release During Motivation, Periodic Reinforcement, Exploratory Behavior, Habit Formation, and Attention", Frontiers in Synaptic Neuroscience, vol. 13, 2021. Available: 10.3389/fnsyn.2021.660218 [Accessed 20 August 2021].

Cited By

Chen MFan WTang WLiu TLi DDib O(2024)Review of Machine Learning Algorithms for Breast Cancer DiagnosisData Mining and Big Data10.1007/978-981-97-0844-4_17(229-243)Online publication date: 22-Feb-2024
https://doi.org/10.1007/978-981-97-0844-4_17
Zhenghan NDib O(2022)Agriculture Stimulates Chinese GDP: A Machine Learning ApproachBig Data Management and Analysis for Cyber Physical Systems10.1007/978-3-031-17548-0_3(21-36)Online publication date: 24-Sep-2022
https://doi.org/10.1007/978-3-031-17548-0_3

Recommendations

Autism Spectrum Disorder Screening: Machine Learning Adaptation and DSM-5 Fulfillment
ICMHI '17: Proceedings of the 1st International Conference on Medical and Health Informatics 2017

One of the primary psychiatric disorders is Autistic Spectrum Disorder (ASD). ASD is a mental disorder that limits the use of linguistic, communicative, cognitive, skills as well as social skills and abilities. Recently, ASD has been studied in the ...
Machine Learning Approaches in Detecting Autism Spectrum Disorder
Abstract
Early detection of Autism Spectrum Disorder (ASD) needs to be increased to prevent further adverse impacts. Thus, the classifi-cation between ASD and Typically Development (TD) individuals is an intriguing task. This review study has collected 26 ...
An inclusive design approach for developing video games for children with Autism Spectrum Disorder

The efficacy of therapeutic treatments for Autism Spectrum Disorder is mainly associated with the treatment's intensity in terms of weekly hours. This has led mental health professionals to explore the use of video games to complement traditional ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

MLMI '21: Proceedings of the 2021 4th International Conference on Machine Learning and Machine Intelligence

September 2021

189 pages

ISBN:9781450384247

DOI:10.1145/3490725

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 December 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

MLMI'21

MLMI'21: 2021 The 4th International Conference on Machine Learning and Machine Intelligence

September 17 - 19, 2021

Hangzhou, China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
134
Total Downloads

Downloads (Last 12 months)41
Downloads (Last 6 weeks)1

Reflects downloads up to 22 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Chen MFan WTang WLiu TLi DDib O(2024)Review of Machine Learning Algorithms for Breast Cancer DiagnosisData Mining and Big Data10.1007/978-981-97-0844-4_17(229-243)Online publication date: 22-Feb-2024
https://doi.org/10.1007/978-981-97-0844-4_17
Zhenghan NDib O(2022)Agriculture Stimulates Chinese GDP: A Machine Learning ApproachBig Data Management and Analysis for Cyber Physical Systems10.1007/978-3-031-17548-0_3(21-36)Online publication date: 24-Sep-2022
https://doi.org/10.1007/978-3-031-17548-0_3

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents