CAMBer: an approach to support comparative analysis of multiple bacterial strains

10.1109/BIBM.2010.5706549Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010121-12

Cost-sensitive decision tree ensembles for effective imbalanced classification

Real-life datasets are often imbalanced, that is, there are significantly more training samples available for some classes than for others, and consequently the conventional aim of reducing overall classification accuracy is not appropriate when dealing with such problems. Various approaches have been introduced in the literature to deal with imbalanced datasets, and are typically based on oversampling, undersampling or cost-sensitive classification. In this paper, we introduce an effective ensemble of cost-sensitive decision trees for imbalanced classification. Base classifiers are constructed according to a given cost matrix, but are trained on random feature subspaces to ensure sufficient diversity of the ensemble members. We employ an evolutionary algorithm for simultaneous classifier selection and assignment of committee member weights for the fusion process. Our proposed algorithm is evaluated on a variety of benchmark datasets, and is confirmed to lead to improved recognition of the minority class, to be capable of outperforming other state-of-the-art algorithms, and hence to represent a useful and effective approach for dealing with imbalanced datasets

lncRNAs-EZH2 interaction as promising therapeutic target in cutaneous melanoma

Melanoma is the most lethal skin cancer with increasing incidence worldwide. Despite a great improvement of diagnostics and treatment of melanoma patients, this disease is still a serious clinical problem. Therefore, novel druggable targets are in focus of research. EZH2 is a component of the PRC2 protein complex that mediates epigenetic silencing of target genes. Several mutations activating EZH2 have been identified in melanoma, which contributes to aberrant gene silencing during tumor progression. Emerging evidence indicates that long non-coding RNAs (lncRNAs) are molecular “address codes” for EZH2 silencing specificity, and targeting lncRNAs-EZH2 interaction may slow down the progression of many solid cancers, including melanoma. This review summarizes current knowledge regarding the involvement of lncRNAs in EZH2-mediated gene silencing in melanoma. The possibility of blocking lncRNAs-EZH2 interaction in melanoma as a novel therapeutic option and plausible controversies and drawbacks of this approach are also briefly discussed

The physiological concentration of ferrous iron (II) alters the inhibitory effect of hydrogen peroxide on CD45, LAR and PTP1B phosphatases

Hydrogen peroxide is an important regulator of protein tyrosine phosphatase activity via reversible oxidation. However, the role of iron in this reaction has not been yet elucidated. Here we compare the influence of hydrogen peroxide and the ferrous iron (reagent for Fenton reaction) on the enzymatic activity of recombinant CD45, LAR, PTP1B phosphatases and cellular CD45 in Jurkat cells. The obtained results show that ferrous iron (II) is potent inhibitor of CD45, LAR and PTP1B, but the inhibitory effect is concentration dependent. We found that the higher concentrations of ferrous iron (II) increase the inactivation of CD45, LAR and PTP1B phosphatase caused by hydrogen peroxide, but the addition of the physiological concentration (500 nM) of ferrous iron (II) has even a slightly preventive effect on the phosphatase activity against hydrogen peroxide

Stringent DDI-based Prediction of H. sapiens-M. tuberculosis H37Rv Protein-Protein Interactions

Background: H. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are very important information to illuminate the infection mechanism of M. tuberculosis H37Rv. But current H. sapiens-M. tuberculosis H37Rv PPI data are very scarce. This seriously limits the study of the interaction between this important pathogen and its host H. sapiens. Computational prediction of H. sapiens-M. tuberculosis H37Rv PPIs is an important strategy to fill in the gap. Domain-domain interaction (DDI) based prediction is one of the frequently used computational approaches in predicting both intra-species and inter-species PPIs. However, the performance of DDI-based host-pathogen PPI prediction has been rather limited. Results: We develop a stringent DDI-based prediction approach with emphasis on (i) differences between the specific domain sequences on annotated regions of proteins under the same domain ID and (ii) calculation of the interaction strength of predicted PPIs based on the interacting residues in their interaction interfaces. We compare our stringent DDI-based approach to a conventional DDI-based approach for predicting PPIs based on gold standard intra-species PPIs and coherent informative Gene Ontology terms assessment. The assessment results show that our stringent DDI-based approach achieves much better performance in predicting PPIs than the conventional approach. Using our stringent DDI-based approach, we have predicted a small set of reliable H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies. We also analyze the H. sapiens-M. tuberculosis H37Rv PPIs predicted by our stringent DDI-based approach using cellular compartment distribution analysis, functional category enrichment analysis and pathway enrichment analysis. The analyses support the validity of our prediction result. Also, based on an analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent DDI-based approach, we have discovered some important properties of domains involved in host-pathogen PPIs. We find that both host and pathogen proteins involved in host-pathogen PPIs tend to have more domains than proteins involved in intra-species PPIs, and these domains have more interaction partners than domains on proteins involved in intra-species PPI. Conclusions: The stringent DDI-based prediction approach reported in this work provides a stringent strategy for predicting host-pathogen PPIs. It also performs better than a conventional DDI-based approach in predicting PPIs. We have predicted a small set of accurate H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies