A Bayesian network perspective on neonatal pneumonia in pregnant women with diabetes mellitus.

OBJECTIVE: To predict the influencing factors of neonatal pneumonia in pregnant women with diabetes mellitus using a Bayesian network model. By examining the intricate network connections between the numerous variables given by Bayesian networks (BN), this study aims to compare the prediction effect of the Bayesian network model and to analyze the influencing factors directly associated to neonatal pneumonia. METHOD: Through the structure learning algorithms of BN, Naive Bayesian (NB), Tree Augmented Naive Bayes (TAN), and k-Dependence Bayesian Classifier (KDB), complex networks connecting variables were presented and their predictive abilities were tested. The BN model and three machine learning models computed using the R bnlean package were also compared in the data set. RESULTS: In constraint-based algorithms, three algorithms had different presentation DAGs. KDB had a better prediction effect than NB and TAN, and it achieved higher AUC compared with TAN. Among three machine learning modes, Support Vector Machine showed a accuracy rate of 91.04% and 67.88% of precision, which was lower than TAN (92.70%; 72.10%). CONCLUSION: KDB was applicable, and it can detect the dependencies between variables, identify more potential associations and track changes between variables and outcome.

Application of DNA molecular marker technologies in study of medicinal Physalis species / 中国中药杂志

As traditional Chinese medicinal herbs, Physalis plants have a variety of pharmacological activities, such as anti-inflammatory, anti-oxidant, and anti-cancer effects, and have been used for the treatment of malaria, rheumatism, hepatitis, asthma, and cancer. In addition to the medicinal value, many Physalis species are also the high-grade nutrition health care fruits, can be made canned and candied etc. In the study, the application progress of DNA molecular marker technologies in medicinal Physalis plants in recent years was reviewed, in order to provide an important molecular technical basis for the identification, classification and rational development and protection of medicinal Physalis resources.