Are gene-by-environment interactions leveraged in multi-modality neural networks for breast cancer prediction?

no code implementations • 30 Jul 2024 • Monica Isgut, Andrew Hornback, Yunan Luo, Asma Khimani, Neha Jain, May D. Wang

While our model did not demonstrate improved performance over the baseline, we discovered 248 (<1%) statistically significant gene-by-gene and gene-by-environment interactions out of the ~53. 6k possible feature pairs, the most contributory of which included rs6001930 (MKL1) and rs889312 (MAP3K1), with age and menopause being the most heavily interacting non-genetic risk factors.

Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review

no code implementations • 23 Dec 2021 • Felipe Giuste, Wenqi Shi, Yuanda Zhu, Tarun Naren, Monica Isgut, Ying Sha, Li Tong, Mitali Gupte, May D. Wang

This systematic review examines the use of Explainable Artificial Intelligence (XAI) during the pandemic and how its use could overcome barriers to real-world success.

Decision Making Experimental Design +2