RESULTS: In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are related to Alzheimer's disease; 2. ZNF329 and RB1 significantly regulate those 'mesenchymal' gene expression signature genes for brain tumors.
CONCLUSION: By merely leveraging gene expression data, CBDN can efficiently infer the existence of gene-gene interactions as well as their regulatory directions. The constructed networks are helpful in the identification of important regulators for complex diseases.
RESULTS: We developed DeSigN, a web-based tool for predicting drug efficacy against cancer cell lines using gene expression patterns. The algorithm correlates phenotype-specific gene signatures derived from differentially expressed genes with pre-defined gene expression profiles associated with drug response data (IC50) from 140 drugs. DeSigN successfully predicted the right drug sensitivity outcome in four published GEO studies. Additionally, it predicted bosutinib, a Src/Abl kinase inhibitor, as a sensitive inhibitor for oral squamous cell carcinoma (OSCC) cell lines. In vitro validation of bosutinib in OSCC cell lines demonstrated that indeed, these cell lines were sensitive to bosutinib with IC50 of 0.8-1.2 μM. As further confirmation, we demonstrated experimentally that bosutinib has anti-proliferative activity in OSCC cell lines, demonstrating that DeSigN was able to robustly predict drug that could be beneficial for tumour control.
CONCLUSIONS: DeSigN is a robust method that is useful for the identification of candidate drugs using an input gene signature obtained from gene expression analysis. This user-friendly platform could be used to identify drugs with unanticipated efficacy against cancer cell lines of interest, and therefore could be used for the repurposing of drugs, thus improving the efficiency of drug development.
METHODS: Six hundred and thirty-six adults with biopsy-proven non-alcoholic fatty liver disease (NAFLD) from two independent Asian cohorts were enrolled in our study. Liver stiffness measurement (LSM) was assessed by vibration-controlled transient elastography (Fibroscan). Fibrotic NASH was defined as NASH with a NAFLD activity score (NAS) ≥ 4 and F ≥ 2 fibrosis.
RESULTS: Metabolic syndrome (MetS), platelet count and MACK-3 were independent predictors of fibrotic NASH. On the basis of their regression coefficients, we developed a novel nomogram showing a good discriminatory ability (area under receiver operating characteristic curve [AUROC]: 0.79, 95% confidence interval [CI 0.75-0.83]) and a high negative predictive value (NPV: 94.7%) to rule out fibrotic NASH. In the validation set, this nomogram had a higher AUROC (0.81, 95%CI 0.74-0.87) than that of MACK-3 (AUROC: 0.75, 95%CI 0.68-0.82; P
METHODS: A 3-phase quasi-experimental community study was conducted from April 2012 to June 2013. Phase l was a cross-sectional study to review the current practice of PNNJ management. Phase ll was an interventional phase involving the implementation of a new protocol. Phase lll was a 6 months post-interventional audit. A registry of PNNJ was implemented to record the incidence rate. A self-reporting surveillance system was put in place to receive any reports of biliary atresia, urinary tract infection, or congenital hypothyroidism cases.
RESULTS: In Phase I, 12 hospitals responded, and 199 case notes were reviewed. In Phase II, a new protocol was developed and implemented in all government health facilities in Perak. In Phase III, the 6-month post-intervention audit showed that there were significant improvements when comparing mean scores of pre- and post-intervention: history taking scores (p
OBJECTIVE: The current study aimed to model and verify the anti-Smo activity of berberine and its derivatives using a novel automated script.
METHOD: Based on the patented inventions filed on ADMET modelling until 2016, which also predicts ADMET parameters and binding efficiency indices for all molecules, a script was developed to run automated molecular docking for a large number of small molecules.
RESULTS: Berberine was found to interact with Lys395 of Smo receptor via hydrogen bonding and cation-π interactions. In addition, π-π interactions between berberine aromatic rings and two aromatic residues in the Smo transmembrane domain, Tyr394 and Phe484, were noted. Binding efficiency indices using an in silico approach to plot the Smo-specific binding potency of each ligand was performed. The mRNA level of Gli1 was studied as the outcome of Hh signalling pathway to show the effect of berberine on hedgehog signalling.
CONCLUSION: This study predicted the role of berberine as an inhibitor of Smo receptor, suggesting its effectiveness in hedgehog signalling during cancer treatment.