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| http://dx.doi.org/10.1038/s41408-020-0307-4 | DOI Listing |
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Integration of Graph Neural Networks and multi-omics analysis identify the predictive factor and key gene for immunotherapy response and prognosis of bladder cancer.
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Medical Cosmetic Center, First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, 515041, People's Republic of China.
Objective: The evaluation of the efficacy of immunotherapy is of great value for the clinical treatment of bladder cancer. Graph Neural Networks (GNNs), pathway analysis and multi-omics analysis have shown great potential in the field of cancer diagnosis and treatment.
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Department of Bioengineering, University of Louisville, Louisville, KY, USA.
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Department of Cardiovascular Surgery, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, China.
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Department of Computer Science, Columbia University, 1214 Amsterdam Ave, 721 Schapiro CEPSR, New York, NY, 10027, USA.
Preeclampsia is one of the leading causes of maternal morbidity, with consequences during and after pregnancy. Because of its diverse clinical presentation, preeclampsia is an adverse pregnancy outcome that is uniquely challenging to predict and manage. In this paper, we developed racial bias-free machine learning models that predict the onset of preeclampsia with severe features or eclampsia at discrete time points in a nulliparous pregnant study cohort.
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