We demonstrate that paired expression profiles of microRNAs (miRNAs) and mRNAs can be used to identify functional miRNA-target relationships with high precision. We used a Bayesian data analysis algorithm, GenMiR++, to identify a network of 1,597 high-confidence target predictions for 104 human miRNAs, which was supported by RNA expression data across 88 tissues and cell types, sequence complementarity and comparative genomics data. We experimentally verified our predictions by investigating the result of let-7b downregulation in retinoblastoma using quantitative reverse transcriptase (RT)-PCR and microarray profiling: some of our verified let-7b targets include CDC25A and BCL7A. Compared to sequence-based predictions, our high-scoring GenMiR++ predictions had much more consistent Gene Ontology annotations and were more accurate predictors of which mRNA levels respond to changes in let-7b levels.

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MicroRNAs (miRNAs) regulate a large proportion of mammalian genes by hybridizing to targeted messenger RNAs (mRNAs) and down-regulating their translation into protein. Although much work has been done in the genome-wide computational prediction of miRNA genes and their target mRNAs, an open question is how to efficiently obtain functional miRNA targets from a large number of candidate miRNA targets predicted by existing computational algorithms. In this paper, we propose a novel Bayesian model and learning algorithm, GenMiR++ (Generative model for miRNA regulation), that accounts for patterns of gene expression using miRNA expression data and a set of candidate miRNA targets. A set of high-confidence functional miRNA targets are then obtained from the data using a Bayesian learning algorithm. Our model scores 467 high-confidence miRNA targets out of 1,770 targets obtained from TargetScanS in mouse at a false detection rate of 2.5%: several confirmed miRNA targets appear in our high-confidence set, such as the interactions between miR-92 and the signal transduction gene MAP2K4, as well as the relationship between miR-16 and BCL2, an anti-apoptotic gene which has been implicated in chronic lymphocytic leukemia. We present results on the robustness of our model showing that our learning algorithm is not sensitive to various perturbations of the data. Our high-confidence targets represent a significant increase in the number of miRNA targets and represent a starting point for a global understanding of gene regulation.

OBJECTIVE: To examine and compare the validity of three known risk-assessment tools (CMQCC [California Maternal Quality Care Collaborative], AWHONN [Association of Women's Health, Obstetric and Neonatal Nurses], and NYSBOH [New York Safety Bundle for Obstetric Hemorrhage]) in women undergoing cesarean delivery. METHODS: We conducted a retrospective cohort study that evaluated all women undergoing cesarean delivery at 23 weeks of gestation or longer from 2012 to 2017 at an urban hospital with average of 1,200 cesarean deliveries per year. Data were obtained by chart review. Severe postpartum hemorrhage was defined as transfusion of at least four units of packed red blood cells during the intrapartum or postpartum period. For each risk-assessment tool, women were stratified into low-risk, medium-risk, and high-risk groups. Risk factors were examined using multivariable logistic regression. RESULTS: Of 6,301 women who underwent cesarean delivery, a total of 76 (1.2%) had severe postpartum hemorrhage. Women classified as low- or medium-risk had lower rates of severe postpartum hemorrhage (0.4–0.6%) compared with women classified as high-risk (1.8–5.1%) (P<.001). Risk factors that were included in all three tools that were associated with severe postpartum hemorrhage included placenta accreta, placenta previa or low-lying placenta, placental abruption, hematocrit less than 30%, and prior uterine scar. Factors included in only one or two tools that were associated with severe postpartum hemorrhage included having more than four previous vaginal deliveries (CMQCC and AWHONN), stillbirth (AWHONN), and more than four prior births (NYSBOH). Area under the curve and 95% CI for CMQCC, AWHONN, and NYSBOH were all moderate—CMQCC 0.77 (0.71–0.84), AWHONN 0.69 (0.65–0.74), and NYSBOH 0.73 (0.67–0.79), respectively (AWHONN being most sensitive [88% with high-risk as cut-off] and CMQCC being most specific [87% with high-risk as cut-off]). CONCLUSIONS: Risk-assessment tools had moderate prediction to identify high-risk groups at risk for severe postpartum hemorrhage after cesarean delivery.