9 research outputs found

    Deep learning-based polygenic risk analysis for Alzheimer's disease prediction

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    BACKGROUND: The polygenic nature of Alzheimer's disease (AD) suggests that multiple variants jointly contribute to disease susceptibility. As an individual's genetic variants are constant throughout life, evaluating the combined effects of multiple disease-associated genetic risks enables reliable AD risk prediction. Because of the complexity of genomic data, current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction. However, deep learning methods, which capture nonlinearity within high-dimensional genomic data, may enable more accurate disease risk prediction and improve our understanding of AD etiology. Accordingly, we developed deep learning neural network models for modeling AD polygenic risk. METHODS: We constructed neural network models to model AD polygenic risk and compared them with the widely used weighted polygenic risk score and lasso models. We conducted robust linear regression analysis to investigate the relationship between the AD polygenic risk derived from deep learning methods and AD endophenotypes (i.e., plasma biomarkers and individual cognitive performance). We stratified individuals by applying unsupervised clustering to the outputs from the hidden layers of the neural network model. RESULTS: The deep learning models outperform other statistical models for modeling AD risk. Moreover, the polygenic risk derived from the deep learning models enables the identification of disease-associated biological pathways and the stratification of individuals according to distinct pathological mechanisms. CONCLUSION: Our results suggest that deep learning methods are effective for modeling the genetic risks of AD and other diseases, classifying disease risks, and uncovering disease mechanisms

    Mechanism of CO2 Emission Reduction by Global Energy Interconnection

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    The challenge of global warming has become a driving force for a global energy transition. The Global Energy Interconnection (GEI) is a modern energy system aimed at meeting the global power demand in a clean and green manner. With the development of clean replacement, electricity replacement, and grid interconnection strategies, GEI contributes to the global temperature control by dramatically reducing the level of energy-related CO2 emissions. This study proposes an integrated framework for analyzing the mechanism of CO2 emission reduction via GEI implementation. The obtained results demonstrate that the total cumulative contribution of GEI to mitigating the effects of CO2 emissions (estimated by conducting a scenario analysis) corresponds to a total reduction of 3100 Gt CO2. The contributions of the clean replacement, electricity replacement, and carbon capture and storage GEI components to this process are equal to 55, 42, 5%, respectively. Using GEI, the utilization of clean energy in 2050 will increase by a factor of 4.5 at an annual growth rate of 4.4%, and the electrification rate will be 2.4 times greater than the current one. Keywords: Global Energy Interconnection, CO2 emission reduction, clean replacement, electricity replacement, grid interconnectio

    Deep learning-based polygenic risk analysis for Alzheimer’s disease prediction

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    Zhou et al. utilise deep learning to improve polygenic risk analysis for Alzheimer’s disease. Their computational approach outperforms existing statistical methods and helps to identify potential biological mechanisms of Alzheimer’s disease risk