PaCMAP-embedded convolutional neural network for multi-omics data integration
Aims: The multi-omics data integration has emerged as a prominent avenue within the healthcare industry, presenting substantial potential for enhancing predictive models. The main motivation behind this study stems from the imperative need to advance prognostic methodologies in cancer diagnosis, an...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023104038 |
| _version_ | 1827367283659374592 |
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| author | Hazem Qattous Mohammad Azzeh Rahmeh Ibrahim Ibrahim Abed Al-Ghafer Mohammad Al Sorkhy Abedalrhman Alkhateeb |
| author_facet | Hazem Qattous Mohammad Azzeh Rahmeh Ibrahim Ibrahim Abed Al-Ghafer Mohammad Al Sorkhy Abedalrhman Alkhateeb |
| author_sort | Hazem Qattous |
| collection | DOAJ |
| description | Aims: The multi-omics data integration has emerged as a prominent avenue within the healthcare industry, presenting substantial potential for enhancing predictive models. The main motivation behind this study stems from the imperative need to advance prognostic methodologies in cancer diagnosis, an area where precision is pivotal for effective clinical decision-making. In this context, the present study introduces an innovative methodology that integrates copy number alteration (CNA), DNA methylation, and gene expression data. Methods: The three omics data were successfully merged into a two-dimensional (2D) map using the PaCMAP dimensionality reduction technique. Utilizing the RGB coloring scheme, a visual representation of the integration was produced utilizing the values of the three omics of each sample. Then, the colored 2D maps were fed into a convolutional neural network (CNN) to forecast the Gleason score. Results: Our proposed model outperforms the cutting-edge i-SOM-GSN model by integrating multi-omics data and the CNN architecture with an accuracy of 98.89, and AUC of 0.9996. Conclusion: This study demonstrates the effectiveness of multi-omics data integration in predicting health outcomes. The proposed methodology, combining PaCMAP for dimensionality reduction, RGB coloring for visualization, and CNN for prediction, offers a comprehensive framework for integrating heterogeneous omics data and improving predictive accuracy. These findings contribute to the advancement of personalized medicine and have the potential to aid in clinical decision-making for prostate cancer patients. |
| first_indexed | 2024-03-08T09:04:20Z |
| format | Article |
| id | doaj.art-5234d402457043ac82924262516411c3 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-08T09:04:20Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-5234d402457043ac82924262516411c32024-02-01T06:30:59ZengElsevierHeliyon2405-84402024-01-01101e23195PaCMAP-embedded convolutional neural network for multi-omics data integrationHazem Qattous0Mohammad Azzeh1Rahmeh Ibrahim2Ibrahim Abed Al-Ghafer3Mohammad Al Sorkhy4Abedalrhman Alkhateeb5Software Engineering Department, Princess Sumaya University for Technology, Amman P.O. Box 1438, Jordan; Corresponding authors.Data Science Department, Princess Sumaya University for Technology, Amman P.O. Box 1438, JordanComputer Science Department, Princess Sumaya University for Technology, Amman P.O. Box 1438, JordanData Science Department, Princess Sumaya University for Technology, Amman P.O. Box 1438, JordanHeritage College of Osteopathic medicine, Ohio University, Cleveland, OH 44122, USAComputer Science Department, Lakehead University, 955 Oliver Rd, Thunder Bay, ON P7B 5E1, Ontario, Canada; Corresponding authors.Aims: The multi-omics data integration has emerged as a prominent avenue within the healthcare industry, presenting substantial potential for enhancing predictive models. The main motivation behind this study stems from the imperative need to advance prognostic methodologies in cancer diagnosis, an area where precision is pivotal for effective clinical decision-making. In this context, the present study introduces an innovative methodology that integrates copy number alteration (CNA), DNA methylation, and gene expression data. Methods: The three omics data were successfully merged into a two-dimensional (2D) map using the PaCMAP dimensionality reduction technique. Utilizing the RGB coloring scheme, a visual representation of the integration was produced utilizing the values of the three omics of each sample. Then, the colored 2D maps were fed into a convolutional neural network (CNN) to forecast the Gleason score. Results: Our proposed model outperforms the cutting-edge i-SOM-GSN model by integrating multi-omics data and the CNN architecture with an accuracy of 98.89, and AUC of 0.9996. Conclusion: This study demonstrates the effectiveness of multi-omics data integration in predicting health outcomes. The proposed methodology, combining PaCMAP for dimensionality reduction, RGB coloring for visualization, and CNN for prediction, offers a comprehensive framework for integrating heterogeneous omics data and improving predictive accuracy. These findings contribute to the advancement of personalized medicine and have the potential to aid in clinical decision-making for prostate cancer patients.http://www.sciencedirect.com/science/article/pii/S2405844023104038Multi-omics data integrationEmbedding techniquesPaCMAPConvolutional neural network |
| spellingShingle | Hazem Qattous Mohammad Azzeh Rahmeh Ibrahim Ibrahim Abed Al-Ghafer Mohammad Al Sorkhy Abedalrhman Alkhateeb PaCMAP-embedded convolutional neural network for multi-omics data integration Heliyon Multi-omics data integration Embedding techniques PaCMAP Convolutional neural network |
| title | PaCMAP-embedded convolutional neural network for multi-omics data integration |
| title_full | PaCMAP-embedded convolutional neural network for multi-omics data integration |
| title_fullStr | PaCMAP-embedded convolutional neural network for multi-omics data integration |
| title_full_unstemmed | PaCMAP-embedded convolutional neural network for multi-omics data integration |
| title_short | PaCMAP-embedded convolutional neural network for multi-omics data integration |
| title_sort | pacmap embedded convolutional neural network for multi omics data integration |
| topic | Multi-omics data integration Embedding techniques PaCMAP Convolutional neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023104038 |
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