Abstract: Disclosed herein are methods of preparing and analyzing samples for biomarkers associated with placenta accreta in pregnant subjects. Also disclosed are method of assessing the risk of placenta accreta in pregnant subjects.

Abstract: A method for developing a longitudinal predictive model for adverse gestational outcomes comprises receiving pregnancy datasets from discrete data sources for a plurality of subjects having a plurality of gestational outcomes, independently analyzing the discrete datasets to identify biomarkers for the gestational outcomes, combining the biomarkers into a single meta-dataset, and analyzing the meta-dataset to produce a model for the gestational outcome. Models can be generated for a plurality of different timepoints in pregnancy. A pregnant subject can be tracked into one of a plurality of treatment tracks based on predicted risk of an adverse gestational outcome after each episode of testing.

Abstract: The present disclosure relates to biomarkers of preterm hirth, biomarkers of term hirth, and methods of use thereof. In particular, the present disclosure provides methods of determining whether a pregnant woman is at an increased risk for premature delivery. The present disclosure further provides methods for decreasing a pregnant woman's risk for premature delivery.

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

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

Abstract: Disclosed herein are methods and systems for determining risk of preeclampsia. The system can include (a) a computer comprising: (i) a processor; and (II) a memory, coupled to the processor, the memory storing a module comprising: (1) test data for a sample from a subject including values indicating a quantitative measure of one or more markers; (2) a classification rule which, based on values including the measurements, classifies the subject as being at risk of preeclampsia, wherein the classification rule is configured to have a sensitivity of at least 75%, at least 85% or at least 95%; and (3) computer executable instructions for implementing the classification rule on the test data.

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.

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

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