MULTIMODAL COMPUTATIONAL FRAMEWORKS FOR ASSESSING GENETIC FACTORS AND BRCA MUTATIONS IN BREAST CANCER
Keywords:
Breast Cancer, BRCA Mutations, Deep Learning, Multimodal Fusion, Polygenic Risk Scores, Genetic ProfilingAbstract
Breast cancer is a highly complex and heterogeneous malignancy driven by an intricate interplay of environmental exposures and genetic factors, most notably mutations in the BRCA1 and BRCA2 genes. Assessing these genetic risks has traditionally relied on isolated statistical methods or manual pedigree analyses, which often struggle to capture the complex, high-dimensional nature of genomic and phenotypic data. This paper explores the landscape of computational approaches, including deep neural networks and multimodal fusion techniques, to predict breast cancer survival and comprehensively identify genetic risk profiles. By integrating advanced machine learning models, we propose a holistic methodology that fuses genetic variant sequences with clinical records and histopathological imaging data to stratify patient risk more accurately. The findings suggest that employing deep learning for polygenic risk scoring and multimodal feature fusion significantly enhances predictive accuracy, ultimately paving the way for highly personalized therapeutic interventions and improved clinical outcomes.
References
Xie, Xinpeng, Li, Yuexiang, & Shen, Linlin (2018). Active Learning for Breast Cancer Identification. https://arxiv.org/pdf/1804.06670v1 https://arxiv.org/pdf/1804.06670v1
Farooq, Muhammad Shoaib, & Ilyas, Mehreen (2023). Predicting environment effects on breast cancer by implementing machine learning. https://arxiv.org/pdf/2309.14397v1 https://arxiv.org/pdf/2309.14397v1
Badré, Adrien, Zhang, Li, Muchero, Wellington, Reynolds, Justin C., & Pan, Chongle (2023). Deep neural network improves the estimation of polygenic risk scores for breast cancer. A. Badré, L. Zhang, W. Muchero, J.C. Reynolds, C. Pan (2021). Deep neural network improves the estimation of polygenic risk scores for breast cancer. Journal of Human Genetics, 66(4), 359-369. https://doi.org/10.1038/s10038-020-00832-7 https://doi.org/10.1038/s10038-020-00832-7
Tirunagari, Santosh, Poh, Norman, Abdulrahman, Hajara, Nemmour, Nawal, & Windridge, David (2015). Breast Cancer Data Analytics With Missing Values: A study on Ethnic, Age and Income Groups. https://arxiv.org/pdf/1503.03680v1 https://arxiv.org/pdf/1503.03680v1
Zuluaga-Gomez, Juan, Masry, Zeina Al, Benaggoune, Khaled, Meraghni, Safa, & Zerhouni, Noureddine (2019). A CNN-based methodology for breast cancer diagnosis using thermal images. https://arxiv.org/pdf/1910.13757v1 https://arxiv.org/pdf/1910.13757v1
Chakraborty, Debajyoti (2023). Screening Mammography Breast Cancer Detection. https://arxiv.org/pdf/2307.11274v1 https://arxiv.org/pdf/2307.11274v1
Good, Benjamin M., Loguercio, Salvatore, Griffith, Obi L., Nanis, Max, Wu, Chunlei, & Su, Andrew I. (2014). The Cure: Making a game of gene selection for breast cancer survival prediction. Good BM, Loguercio S, Griffith OL, Nanis M, Wu C, Su AI The Cure: Design and Evaluation of a Crowdsourcing Game for Gene Selection for Breast Cancer Survival Prediction JMIR Serious Games 2014;2(2):e7. https://doi.org/10.2196/games.3350 https://doi.org/10.2196/games.3350
Divyashree, BV, R, Amarnath, M, Naveen, & Kumar, G Hemantha (2018). Novel approach to locate region of interest in mammograms for Breast cancer. International Journal of Intelligent Systems and Applications in Engineering.(ISSN:2147-6799) Vol 6, No 3 (2018). https://doi.org/10.18201/ijisae.2018644775 https://doi.org/10.18201/ijisae.2018644775
Mondol, Raktim Kumar, Millar, Ewan K. A., Sowmya, Arcot, & Meijering, Erik (2024). BioFusionNet: Deep Learning-Based Survival Risk Stratification in ER+ Breast Cancer Through Multifeature and Multimodal Data Fusion. JBHI, 24 June 2024. https://doi.org/10.1109/JBHI.2024.3418341 https://doi.org/10.1109/JBHI.2024.3418341
Mondol, Raktim Kumar, Millar, Ewan K. A., Sowmya, Arcot, & Meijering, Erik (2024). MM-SurvNet: Deep Learning-Based Survival Risk Stratification in Breast Cancer Through Multimodal Data Fusion. https://arxiv.org/pdf/2402.11788v1 https://arxiv.org/pdf/2402.11788v1
