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Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning

, , , , , , , , , , and . Frontiers in Pediatrics, (2022)

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Industrial perspective on reuse of safety artifacts in software product lines., , , , and . SPLC (A), page 23:1-23:12. ACM, (2019)Robust dimensionality reduction for data visualization with deep neural networks., , , and . Graph. Model., (2020)Towards Source-Level Timing Analysis of Embedded Software Using Functional Verification Methods.. Technical University of Munich, Germany, (2020)Toponomics method for the automated quantification of membrane protein translocation., , , , , , and . BMC Bioinform., (2011)Data-Driven Modeling of Pregnancy-Related Complications, , , , , , , , , and 7 other author(s). Trends in Molecular Medicine, (2021)Machine Learning Techniques, , and . page 147--164. Springer International Publishing, Cham, (2022)Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning, , , , , , , , , and 1 other author(s). Frontiers in Pediatrics, (2022)In-Silico Generation of High-Dimensional Immune Response Data in Patients using a Deep Neural Network, , , , , , , , , and 1 other author(s). Cytometry Part A, (2022)Good Change and Bad Change: An Analysis Perspective on Software Evolution., , , , and . LNCS Trans. Found. Mastering Chang., (2016)Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity, , , , , , , , , and 1 other author(s). npj Digital Medicine, 6 (1): 171 (2023)