Abstract: The present disclosure relates to proteomic biomarkers of spontaneous preterm birth, proteomic biomarkers of term birth, and methods of use thereof. In particular, the present disclosure provides tools for determining whether a pregnant subject is at an increased risk for premature delivery, as well as tools for decreasing a pregnant subject's risk for premature delivery.

Abstract: The present disclosure relates to systems and methods of using machine learning analysis to stratify the risk of spontaneous preterm birth (SPTB). In some variations, to select informative markers that differentiate SPTB from term deliveries, a processed quantification data of the markers can be subjected to univariate receiver operating characteristic (ROC) curve analysis. A Differential Dependency Network (DDN) can then applied in order to extract co-expression patterns among the markers. In order to assess the complementary values among selected markers and the range of their relevant performance, multivariate linear models can be derived and evaluated using bootstrap resampling.

Abstract: Provided herein are proteomic biomarkers of spontaneous preterm birth, proteomic biomarkers of term birth, and methods of use thereof. In particular, provided are tools for determining whether a pregnant subject is at an increased risk for premature delivery, as well as tools for decreasing a pregnant subject's risk for premature delivery.

Abstract: The present disclosure relates to proteomic biomarkers of spontaneous preterm birth, proteomic biomarkers of term birth, and methods of use thereof. In particular, the present disclosure provides tools for determining whether a pregnant subject is at an increased risk for premature delivery, as well as tools for decreasing a pregnant subject's risk for premature delivery.