Predicting potential miRNA-disease associations based on more reliable negative sample selection.

BACKGROUND: Increasing biomedical studies have shown that the dysfunction of miRNAs is closely related with many human diseases. Identifying disease-associated miRNAs would contribute to the understanding of pathological mechanisms of diseases. Supervised learning-based computational methods have continuously been developed for miRNA-disease association predictions. Negative samples of experimentally-validated uncorrelated miRNA-disease pairs are required for these approaches, while they are not available due to lack of biomedical research interest. Existing methods mainly choose negative samples from the unlabelled ones randomly. Therefore, the selection of more reliable negative samples is of great importance for these methods to achieve satisfactory prediction results. RESULTS: In this study, we propose a computational method termed as KR-NSSM which integrates two semi-supervised algorithms to select more reliable negative samples for miRNA-disease association predictions. Our method uses a refined K-means algorithm for preliminary screening of likely negative and positive miRNA-disease samples. A Rocchio classification-based method is applied for further screening to receive more reliable negative and positive samples. We implement ablation tests in KR-NSSM and find that the combination of the two selection procedures would obtain more reliable negative samples for miRNA-disease association predictions. Comprehensive experiments based on fivefold cross-validations demonstrate improvements in prediction accuracy on six classic classifiers and five known miRNA-disease association prediction models when using negative samples chose by our method than by previous negative sample selection strategies. Moreover, 469 out of 1123 selected positive miRNA-disease associations by our method are confirmed by existing databases. CONCLUSIONS: Our experiments show that KR-NSSM can screen out more reliable negative samples from the unlabelled ones, which greatly improves the performance of supervised machine learning methods in miRNA-disease association predictions. We expect that KR-NSSM would be a useful tool in negative sample selection in biomedical research.

Comparative analysis of similarity measurements in miRNAs with applications to miRNA-disease association predictions.

BACKGROUND: As regulators of gene expression, microRNAs (miRNAs) are increasingly recognized as critical biomarkers of human diseases. Till now, a series of computational methods have been proposed to predict new miRNA-disease associations based on similarity measurements. Different categories of features in miRNAs are applied in these methods for miRNA-miRNA similarity calculation. Benchmarking tests on these miRNA similarity measures are warranted to assess their effectiveness and robustness. RESULTS: In this study, 5 categories of features, i.e. miRNA sequences, miRNA expression profiles in cell-lines, miRNA expression profiles in tissues, gene ontology (GO) annotations of miRNA target genes and Medical Subject Heading (MeSH) terms of miRNA-associated diseases, are collected and similarity values between miRNAs are quantified based on these feature spaces, respectively. We systematically compare the 5 similarities from multi-statistical views. Furthermore, we adopt a rule-based inference method to test their performance on miRNA-disease association predictions with the similarity measurements. Comprehensive comparison is made based on leave-one-out cross-validations and a case study. Experimental results demonstrate that the similarity measurement using MeSH terms performs best among the 5 measurements. It should be noted that the other 4 measurements can also achieve reliable prediction performance. The best-performed similarity measurement is used for new miRNA-disease association predictions and the inferred results are released for further biomedical screening. CONCLUSIONS: Our study suggests that all the 5 features, even though some are restricted by data availability, are useful information for inferring novel miRNA-disease associations. However, biased prediction results might be produced in GO- and MeSH-based similarity measurements due to incomplete feature spaces. Similarity fusion may help produce more reliable prediction results. We expect that future studies will provide more detailed information into the 5 feature spaces and widen our understanding about disease pathogenesis.

Comparative genomics analysis of Stenotrophomonas maltophilia strains from a community.

Background: Stenotrophomonas maltophilia is a multidrug-resistant (MDR) opportunistic pathogen with high resistance to most clinically used antimicrobials. The dissemination of MDR S. maltophilia and difficult treatment of its infection in clinical settings are global issues. Methods: To provide more genetic information on S. maltophilia and find a better treatment strategy, we isolated five S. maltophilia, SMYN41-SMYN45, from a Chinese community that were subjected to antibiotic susceptibility testing, biofilm formation assay, and whole-genome sequencing. Whole-genome sequences were compared with other thirty-seven S. maltophilia sequences. Results: The five S. maltophilia strains had similar antibiotic resistance profiles and were resistant to ß-lactams, aminoglycosides, and macrolides. They showed similar antimicrobial resistance (AMR) genes, including various efflux pumps, ß-lactamase resistance genes (blaL1/2), aminoglycoside resistance genes [aac(6'), aph(3'/6)], and macrolide-resistant gene (MacB). Genome sequencing analysis revealed that SMYN41-SMYN45 belonged to sequence type 925 (ST925), ST926, ST926, ST31, and ST928, respectively, and three new STs were identified (ST925, ST926, and ST928). Conclusion: This study provides genetic information by comparing genome sequences of several S. maltophilia isolates from a community of various origins, with the aim of optimizing empirical antibiotic medication and contributing to worldwide efforts to tackle antibiotic resistance.