Abstract
Cancer is a major threat to humankind and a leading cause of mortality worldwide. According to World Health Organization estimates in 2020, breast cancer is a top-tier cancer and a substantial cause of death in women. The early identification of breast cancer can effectively reduce risk factors and mortality. Recent studies in risk analysis were focused on the stand-alone impact of genomic data ignoring the influence of environmental factors. In our proposed method, we studied the breast cancer risk assessment using multi-omics with epigenetic factors and a deep learning model. Our model outperformed existing breast cancer detection methods and stage identification on data collected from TCGA-GDC datahub. However, the vitality analysis model could not produce significant results mainly due to non-availability of sufficient quality data on survival information. The proposed model validates the crucial role of DNA methylation in pre-symptomatic breast cancer risk analysis.
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Gireesh Kumar, M., Aparna, P., Gopakumar, G. (2024). Breast Cancer Risk Analysis Using Deep Learning on Multi-omics Data Combined with Epigenetic Factors. In: Pino, E., Magjarević, R., de Carvalho, P. (eds) International Conference on Biomedical and Health Informatics 2022. ICBHI 2022. IFMBE Proceedings, vol 108. Springer, Cham. https://doi.org/10.1007/978-3-031-59216-4_4
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DOI: https://doi.org/10.1007/978-3-031-59216-4_4
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