%0 Journal Article %1 espinosa2021datadriven %A Espinosa, Camilo %A Becker, Martin %A Maric, Ivana %A Wong, Ronald J. %A Shaw, Gary M. %A Gaudilliere, Brice %A Aghaeepour, Nima %A Stevenson, David K. %A Stelzer, Ina A. %A Peterson, Laura S. %A Chang, Alan L. %A Xenochristou, Maria %A Phongpreecha, Thanaphong %A De Francesco, Davide %A Katz, Michael %A Blumenfeld, Yair J. %A Angst, Martin S. %D 2021 %J Trends in Molecular Medicine %K approach biology cofirst complications data driven holistic medicine modeling myown p21 pregnancy project:bmbf systems %R https://doi.org/10.1016/j.molmed.2021.01.007 %T Data-Driven Modeling of Pregnancy-Related Complications %U https://www.sciencedirect.com/science/article/pii/S1471491421000393 %X A healthy pregnancy depends on complex interrelated biological adaptations involving placentation, maternal immune responses, and hormonal homeostasis. Recent advances in high-throughput technologies have provided access to multiomics biological data that, combined with clinical and social data, can provide a deeper understanding of normal and abnormal pregnancies. Integration of these heterogeneous datasets using state-of-the-art machine-learning methods can enable the prediction of short- and long-term health trajectories for a mother and offspring and the development of treatments to prevent or minimize complications. We review advanced machine-learning methods that could: provide deeper biological insights into a pregnancy not yet unveiled by current methodologies; clarify the etiologies and heterogeneity of pathologies that affect a pregnancy; and suggest the best approaches to address disparities in outcomes affecting vulnerable populations.

@article{espinosa2021datadriven, abstract = {A healthy pregnancy depends on complex interrelated biological adaptations involving placentation, maternal immune responses, and hormonal homeostasis. Recent advances in high-throughput technologies have provided access to multiomics biological data that, combined with clinical and social data, can provide a deeper understanding of normal and abnormal pregnancies. Integration of these heterogeneous datasets using state-of-the-art machine-learning methods can enable the prediction of short- and long-term health trajectories for a mother and offspring and the development of treatments to prevent or minimize complications. We review advanced machine-learning methods that could: provide deeper biological insights into a pregnancy not yet unveiled by current methodologies; clarify the etiologies and heterogeneity of pathologies that affect a pregnancy; and suggest the best approaches to address disparities in outcomes affecting vulnerable populations.}, added-at = {2021-06-20T19:56:18.000+0200}, author = {Espinosa, Camilo and Becker, Martin and Maric, Ivana and Wong, Ronald J. and Shaw, Gary M. and Gaudilliere, Brice and Aghaeepour, Nima and Stevenson, David K. and Stelzer, Ina A. and Peterson, Laura S. and Chang, Alan L. and Xenochristou, Maria and Phongpreecha, Thanaphong and {De Francesco}, Davide and Katz, Michael and Blumenfeld, Yair J. and Angst, Martin S.}, biburl = {https://www.bibsonomy.org/bibtex/269c3a5560be5c636d401ffd2f20267ad/becker}, description = {impactfactor = {11.951}, impactfactor-year = 2021, impactfactor-source = {https://academic-accelerator.com/Impact-of-Journal/Trends-in-Molecular-Medicine}}, doi = {https://doi.org/10.1016/j.molmed.2021.01.007}, interhash = {eb05530806316a4f8a2dbc37df2fe49a}, intrahash = {69c3a5560be5c636d401ffd2f20267ad}, issn = {1471-4914}, journal = {Trends in Molecular Medicine}, keywords = {approach biology cofirst complications data driven holistic medicine modeling myown p21 pregnancy project:bmbf systems}, timestamp = {2022-02-22T01:42:47.000+0100}, title = {Data-Driven Modeling of Pregnancy-Related Complications}, url = {https://www.sciencedirect.com/science/article/pii/S1471491421000393}, year = 2021 }