Voting-based consensus clustering for combining multiple clusterings of chemical structures

<p>Abstract</p> <p>Background</p> <p>Although many consensus clustering methods have been successfully used for combining multiple classifiers in many areas such as machine learning, applied statistics, pattern recognition and bioinformatics, few consensus clustering methods have been applied for combining multiple clusterings of chemical structures. It is known that any individual clustering method will not always give the best results for all types of applications. So, in this paper, three voting and graph-based consensus clusterings were used for combining multiple clusterings of chemical structures to enhance the ability of separating biologically active molecules from inactive ones in each cluster.</p> <p>Results</p> <p>The cumulative voting-based aggregation algorithm (CVAA), cluster-based similarity partitioning algorithm (CSPA) and hyper-graph partitioning algorithm (HGPA) were examined. The F-measure and Quality Partition Index method (QPI) were used to evaluate the clusterings and the results were compared to the Ward’s clustering method. The MDL Drug Data Report (MDDR) dataset was used for experiments and was represented by two 2D fingerprints, ALOGP and ECFP_4. The performance of voting-based consensus clustering method outperformed the Ward’s method using F-measure and QPI method for both ALOGP and ECFP_4 fingerprints, while the graph-based consensus clustering methods outperformed the Ward’s method only for ALOGP using QPI. The Jaccard and Euclidean distance measures were the methods of choice to generate the ensembles, which give the highest values for both criteria.</p> <p>Conclusions</p> <p>The results of the experiments show that consensus clustering methods can improve the effectiveness of chemical structures clusterings. The cumulative voting-based aggregation algorithm (CVAA) was the method of choice among consensus clustering methods.</p

Efficacy and safety of parecoxib in the treatment of acute renal colic: a randomized clinical trial

PURPOSE: Although nonselective nonsteroidal anti-inflammatory drugs (nsNSAIDs) and opioids are effective treatments for acute renal colic, they are associated with adverse events (AEs). As cyclooxygenase-2 selective NSAIDs may provide a safer alternative, we compared the efficacy and safety of parecoxib versus an nsNSAID in subjects with acute renal colic. MATERIALS AND METHODS: Phase IV., multicenter, double-blind, noninferiority, active-controlled study: 338 subjects with acute renal colic were randomized to parecoxib 40 mg i.v. plus placebo (n = 174) or ketoprofen 100 mg IV plus placebo (n = 164). 338 subjects with acute renal colic were randomized to parecoxib 40 mg IV (n = 174) or ketoprofen 100 mg IV(n = 164) plus placebo. Subjects were evaluated 15, 30, 45, 60, 90 and 120 minutes after treatment start and 24 hours after discharge. Primary endpoint was the mean pain intensity difference (PID) at 30 minutes by visual analog scale (VAS) (per-protocol population). An ANCOVA model was used with treatment group, country, and baseline score as covariates. Non-inferiority of parecoxib to ketoprofen was declared if the lower bound of the 95% confidence interval (CI) for the difference between the two groups excluded the pre-established margin of 10 mm for the primary endpoint. RESULTS: Baseline demographics were similar. The mean (SD) mPID30 min was 33.84 (24.61) and 35.16 (26.01) for parecoxib and ketoprofen, respectively. For treatment difference (parecoxib-ketoprofen) the lower bound of the 95% CI was 6.53. The mean change from baseline in VAS 30 minutes after study medication was ~43 mm; AEs were comparable between treatments. CONCLUSIONS: Parecoxib is as effective as ketoprofen in the treatment of pain due to acute renal colic, is well tolerated, and has a comparable safety profile

Advances in the use of terminal restriction fragment length polymorphism (T-RFLP) analysis of 16S rRNA genes to characterize microbial communities

Terminal restriction fragment length polymorphism (T-RFLP) analysis is a popular high-throughput fingerprinting technique used to monitor changes in the structure and composition of microbial communities. This approach is widely used because it offers a compromise between the information gained and labor intensity. In this review, we discuss the progress made in T-RFLP analysis of 16S rRNA genes and functional genes over the last 10 years and evaluate the performance of this technique when used in conjunction with different statistical methods. Web-based tools designed to perform virtual polymerase chain reaction and restriction enzyme digests greatly facilitate the choice of primers and restriction enzymes for T-RFLP analysis. Significant improvements have also been made in the statistical analysis of T-RFLP profiles such as the introduction of objective procedures to distinguish between signal and noise, the alignment of T-RFLP peaks between profiles, and the use of multivariate statistical methods to detect changes in the structure and composition of microbial communities due to spatial and temporal variation or treatment effects. The progress made in T-RFLP analysis of 16S rRNA and genes allows researchers to make methodological and statistical choices appropriate for the hypotheses of their studies.Ursel M. E. Schütte, Zaid Abdo, Stephen J. Bent, Conrad Shyu, Christopher J. Williams, Jacob D. Pierson, Larry J. Forne