Treatment of missing data in Bayesian network structure learning: an application to linked biomedical and social survey data.

BACKGROUND: Availability of linked biomedical and social science data has risen dramatically in past decades, facilitating holistic and systems-based analyses. Among these, Bayesian networks have great potential to tackle complex interdisciplinary problems, because they can easily model inter-relations between variables. They work by encoding conditional independence relationships discovered via advanced inference algorithms. One challenge is dealing with missing data, ubiquitous in survey or biomedical datasets. Missing data is rarely addressed in an advanced way in Bayesian networks; the most common approach is to discard all samples containing missing measurements. This can lead to biased estimates. Here, we examine how Bayesian network structure learning can incorporate missing data. METHODS: We use a simulation approach to compare a commonly used method in frequentist statistics, multiple imputation by chained equations (MICE), with one specific for Bayesian network learning, structural expectation-maximization (SEM). We simulate multiple incomplete categorical (discrete) data sets with different missingness mechanisms, variable numbers, data amount, and missingness proportions. We evaluate performance of MICE and SEM in capturing network structure. We then apply SEM combined with community analysis to a real-world dataset of linked biomedical and social data to investigate associations between socio-demographic factors and multiple chronic conditions in the US elderly population. RESULTS: We find that applying either method (MICE or SEM) provides better structure recovery than doing nothing, and SEM in general outperforms MICE. This finding is robust across missingness mechanisms, variable numbers, data amount and missingness proportions. We also find that imputed data from SEM is more accurate than from MICE. Our real-world application recovers known inter-relationships among socio-demographic factors and common multimorbidities. This network analysis also highlights potential areas of investigation, such as links between cancer and cognitive impairment and disconnect between self-assessed memory decline and standard cognitive impairment measurement. CONCLUSION: Our simulation results suggest taking advantage of the additional information provided by network structure during SEM improves the performance of Bayesian networks; this might be especially useful for social science and other interdisciplinary analyses. Our case study show that comorbidities of different diseases interact with each other and are closely associated with socio-demographic factors.

Baicalein induces the apoptosis of HCT116 human colon cancer cells via the upregulation of DEPP/Gadd45a and activation of MAPKs.

Baicalein has efficient antitumor properties and has been reported to promote the apoptosis of several human cancer cell lines. Decidual protein induced by progesterone (DEPP), a transcriptional target of Forkhead Box O, was originally identified from the human endometrial stromal cell cDNA library. However, the expression and physiological functions of DEPP in human colon cancer cells remain to be fully elucidated. In the present study, it was reported that baicalein stimulated apoptosis and morphological changes of HCT116, A549 and Panc­1 cells in a dose-dependent manner. It also upregulated the mRNA and protein levels of DEPP and growth arrest and DNA damage-inducible 45α (Gadd45a). In addition, the overexpression of DEPP promoted mitogen-activated protein kinase (MAPK) phosphorylation. To further investigate the role of DEPP and Gadd45a in baicalein-induced apoptosis, HCT116 cells were transfected with small interfering RNA against either DEPP or Gadd45a as in vitro models. Through an Annexin V/PI double staining assay, it was observed that baicalein-induced apoptosis was impaired by the inactivation of either DEPP or Gadd45a, which in turn restricted the baicalein-induced activation of caspase­3 and caspase­9 and phosphorylation of MAPKs. In addition, the inhibition of c­Jun N­terminal kinase (JNK)/p38 activity with SP600125/SB203580 decreased the expression of Gadd45a, whereas the inactivation of extracellular signal-regulated kinase with SCH772984 had no effect on the expression of Gadd45a. Taken together, these results demonstrated that baicalein induced the upregulation of DEPP and Gadd45a, which promoted the activation of MAPKs with a positive feedback loop between Gadd45a and JNK/p38, resulting in a marked apoptotic response in human colon cancer cells. These results indicated that baicalein is a potential antitumor drug for the treatment of colon cancer.