Amyotrophic lateral sclerosis diagnosis using machine learning and multi-omic data integration
Amyotrophic Lateral Sclerosis (ALS) is a complex and rare neurodegenerative disorder characterized by significant genetic, molecular, and clinical heterogeneity. Despite numerous endeavors to discover the genetic factors underlying ALS, a significant number of these factors remain unknown. This know...
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Elsevier
2024-10-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024146142 |
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| author | Hima Nikafshan Rad Zheng Su Anne Trinh M.A. Hakim Newton Jannah Shamsani NYGC ALS Consortium Abdul Karim Abdul Sattar |
| author_facet | Hima Nikafshan Rad Zheng Su Anne Trinh M.A. Hakim Newton Jannah Shamsani NYGC ALS Consortium Abdul Karim Abdul Sattar |
| author_sort | Hima Nikafshan Rad |
| collection | DOAJ |
| description | Amyotrophic Lateral Sclerosis (ALS) is a complex and rare neurodegenerative disorder characterized by significant genetic, molecular, and clinical heterogeneity. Despite numerous endeavors to discover the genetic factors underlying ALS, a significant number of these factors remain unknown. This knowledge gap highlights the necessity for personalized medicine approaches that can provide more comprehensive information for the purposes of diagnosis, prognosis, and treatment of ALS. This work utilizes an innovative approach by employing a machine learning-facilitated, multi-omic model to develop a more comprehensive knowledge of ALS. Through unsupervised clustering on gene expression profiles, 9,847 genes associated with ALS pathways are isolated and integrated with 7,699 genes containing rare, presumed pathogenic genomic variants, leading to a comprehensive amalgamation of 17,546 genes. Subsequently, a Variational Autoencoder is applied to distil complex biomedical information from these genes, culminating in the creation of the proposed Multi-Omics for ALS (MOALS) model, which has been designed to expose intricate genotype-phenotype interconnections within the dataset. Our meticulous investigation elucidates several pivotal ALS signaling pathways and demonstrates that MOALS is a superior model, outclassing other machine learning models based on single omic approaches such as SNV and RNA expression, enhancing accuracy by 1.7 percent and 6.2 percent, respectively. The findings of this study suggest that analyzing the relationships within biological systems can provide heuristic insights into the biological mechanisms that help to make highly accurate ALS diagnosis tools and achieve more interpretable results. |
| format | Article |
| id | doaj-art-7e2f975503534e4c99f983ea15ba8060 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-7e2f975503534e4c99f983ea15ba80602024-11-12T05:19:07ZengElsevierHeliyon2405-84402024-10-011020e38583Amyotrophic lateral sclerosis diagnosis using machine learning and multi-omic data integrationHima Nikafshan Rad0Zheng Su1Anne Trinh2M.A. Hakim Newton3Jannah Shamsani4 NYGC ALS Consortium5Abdul Karim6Abdul Sattar7School of Information and Communication Technology, Griffith University, 170 Kessels Rd, Nathan, Brisbane, 4111, QLD, Australia; Corresponding author.GenieUs Genomics Pty Ltd, Sydney, 2000, NSW, Australia; School of Biotechnology and Biomolecular Sciences, Faculty of Science, The University of New South Wales, Sydney, 2052, NSW, AustraliaGenieUs Genomics Pty Ltd, Sydney, 2000, NSW, AustraliaSchool of Information and Physical Sciences, The University of Newcastle, University Drive, Callaghan, Newcastle, 2308, NSW, AustraliaGenieUs Genomics Pty Ltd, Sydney, 2000, NSW, AustraliaThe New York Genome Center, 101 Avenue of the Americas, New York, 10013, NY, USASchool of Information and Communication Technology, Griffith University, 170 Kessels Rd, Nathan, Brisbane, 4111, QLD, AustraliaSchool of Information and Communication Technology, Griffith University, 170 Kessels Rd, Nathan, Brisbane, 4111, QLD, Australia; Institute of Integrated and Intelligent Systems, Griffith University, 170 Kessels Rd, Nathan, Brisbane, 4111, QLD, AustraliaAmyotrophic Lateral Sclerosis (ALS) is a complex and rare neurodegenerative disorder characterized by significant genetic, molecular, and clinical heterogeneity. Despite numerous endeavors to discover the genetic factors underlying ALS, a significant number of these factors remain unknown. This knowledge gap highlights the necessity for personalized medicine approaches that can provide more comprehensive information for the purposes of diagnosis, prognosis, and treatment of ALS. This work utilizes an innovative approach by employing a machine learning-facilitated, multi-omic model to develop a more comprehensive knowledge of ALS. Through unsupervised clustering on gene expression profiles, 9,847 genes associated with ALS pathways are isolated and integrated with 7,699 genes containing rare, presumed pathogenic genomic variants, leading to a comprehensive amalgamation of 17,546 genes. Subsequently, a Variational Autoencoder is applied to distil complex biomedical information from these genes, culminating in the creation of the proposed Multi-Omics for ALS (MOALS) model, which has been designed to expose intricate genotype-phenotype interconnections within the dataset. Our meticulous investigation elucidates several pivotal ALS signaling pathways and demonstrates that MOALS is a superior model, outclassing other machine learning models based on single omic approaches such as SNV and RNA expression, enhancing accuracy by 1.7 percent and 6.2 percent, respectively. The findings of this study suggest that analyzing the relationships within biological systems can provide heuristic insights into the biological mechanisms that help to make highly accurate ALS diagnosis tools and achieve more interpretable results.http://www.sciencedirect.com/science/article/pii/S2405844024146142ALS diagnosisPathway level analysisVariational autoencoderMulti-omic integration |
| spellingShingle | Hima Nikafshan Rad Zheng Su Anne Trinh M.A. Hakim Newton Jannah Shamsani NYGC ALS Consortium Abdul Karim Abdul Sattar Amyotrophic lateral sclerosis diagnosis using machine learning and multi-omic data integration Heliyon ALS diagnosis Pathway level analysis Variational autoencoder Multi-omic integration |
| title | Amyotrophic lateral sclerosis diagnosis using machine learning and multi-omic data integration |
| title_full | Amyotrophic lateral sclerosis diagnosis using machine learning and multi-omic data integration |
| title_fullStr | Amyotrophic lateral sclerosis diagnosis using machine learning and multi-omic data integration |
| title_full_unstemmed | Amyotrophic lateral sclerosis diagnosis using machine learning and multi-omic data integration |
| title_short | Amyotrophic lateral sclerosis diagnosis using machine learning and multi-omic data integration |
| title_sort | amyotrophic lateral sclerosis diagnosis using machine learning and multi omic data integration |
| topic | ALS diagnosis Pathway level analysis Variational autoencoder Multi-omic integration |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024146142 |
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