Multi-omics Data Integration Model Based on UMAP Embedding and Convolutional Neural Network. (September 2022)

• Record Type: Journal Article
• Authors: ElKarami, Bashier; Alkhateeb, Abedalrhman; Qattous, Hazem; Alshomali, Lujain; Shahrrava, Behnam
• Is Part Of: Cancer informatics. Volume 21(2022)
• Publication Date: 2022-09
• Subjects: Multi-omics data integration -- deep learning -- UMAP -- data embedding -- cancer; Bioinformatics -- Periodicals; Biology -- Data processing -- Periodicals; Cancer -- Periodicals; Cancer -- Research -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1177/11769351221124205
• Languages: English
• ISSNs: 1176-9351

Abstract

Introduction: Multi-omics data integration facilitates collecting richer understanding and perceptions than separate omics data. Various promising integrative approaches have been utilized to analyze multi-omics data for biomedical applications, including disease prediction and disease subtypes, biomarker prediction, and others. Methods: In this paper, we introduce a multi-omics data integration method that is constructed using the combination of gene similarity network (GSN) based on uniform manifold approximation and projection (UMAP) and convolutional neural networks (CNNs). The method utilizes UMAP to embed gene expression, DNA methylation, and copy number alteration (CNA) to a lower dimension creating two-dimensional RGB images. Gene expression is used as a reference to construct the GSN and then integrate other omics data with the gene expression for better prediction. We used CNNs to predict the Gleason score levels of prostate cancer patients and the tumor stage in breast cancer patients. Results: The model proposed near perfection with accuracy above 99% with all other performance measurements at the same level. The proposed model outperformed the state-of-art iSOM-GSN model that constructs the GSN map based on the self-organizing map. Conclusion: The results show that UMAP as an embedding technique can better integrate multi-omics maps into the prediction model than SOM. The proposed model can also be applied to build a multi-omics prediction model for other types of cancer.