Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration
Open accessAbstract
Gene expression analysis plays a significant role for providing molecular insights in cancer. Various genetic and epigenetic factors (being dealt under multi-omics) affect gene expression giving rise to cancer phenotypes. A recent growth in understanding of multi-omics seems to provide a resource for integration in interdisciplinary biology since they altogether can draw the comprehensive picture of an organism's developmental and disease biology in cancers. Such large scale multi-omics data can be obtained from public consortium like The Cancer Genome Atlas (TCGA) and several other platforms. Integrating these multi-omics data from varied platforms is still challenging due to high noise and sensitivity of the platforms used. Currently, a robust integrative predictive model to estimate gene expression from these genetic and epigenetic data is lacking. In this study, we have developed a deep learning-based predictive model using Deep Denoising Auto-encoder (DDAE) and Multi-layer Perceptron (MLP) that can quantitatively capture how genetic and epigenetic alterations correlate with directionality of gene expression for liver hepatocellular carcinoma (LIHC). The DDAE used in the study has been trained to extract significant features from the input omics data to estimate the gene expression. These features have then been used for back-propagation learning by the multilayer perceptron for the task of regression and classification. We have benchmarked the proposed model against state-of-the-art regression models. Finally, the deep learning-based integration model has been evaluated for its disease classification capability, where an accuracy of 95.1% has been obtained.
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Most current literature applies deep learning to large-scale multi-omics datasets from open sources such as TCGA, CCLE and GDSC for cancer prognosis and anti-cancer drug response.
“This fact is reinforced by the evidence that most of the current literature employs deep learning on large-scale multi-omics datasets from open sources such as TCGA 3 , CCLE 4 and GDSC 5 for cancer prognosis (Poirion et al., 2018;Seal et al., 2020;Tong et al., 2020;Lee et al., 2020;Zhu et al., 2020) and anti-cancer drug response (Sharifi-Noghabi et al., 2019;Li et al., 2019;Deng et al., 2020).”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Seal et al., 2020
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- Authors
- Seal, D. B., Das, V., Goswami, S., & De, R. K.
- Affiliations
- University of Calcutta (IN)
- Novo Nordisk (United States) (US)
- Bangabasi Morning College (IN)
- Indian Statistical Institute (IN)
- Corresponding author
- De, Rajat K.
- Venue
- Genomics
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 112
- Issue
- 4
- Pages
- 2833-2841
- ISSN
- 0888-7543 (ISSN-L)
- OpenAlex
- W3013844559
- Cited by
- 69
- References
- 61
- Citations by year
- 2026: 10 · 2025: 11 · 2024: 8 · 2023: 13 · 2022: 12 · 2021: 13 · 2020: 2
- FWCI
- 3.24
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Genomics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Department of Biotechnology, Government of India (BT/Med-II/NIBMG/SyMeC/2014/Vol. II)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- gene expression estimation, DNA methylation, copy number variation, multi-omics integration, deep denoising autoencoder, multilayer perceptron, hepatocellular carcinoma, cancer classification, epigenetic modifications, transcriptome, TCGA
- MeSH terms
- Deep Learning, RNA-Seq, Carcinoma, Hepatocellular, Liver Neoplasms, Linear Models, DNA Methylation, Genomics, DNA Copy Number Variations, Epigenomics, Transcriptome
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