A study of performance on microarray data sets for a classifier based on information theoretic learning

A new DNA sequence entropy-based Kullback-Leibler algorithm for gene clustering

Information theory is a branch of mathematics that overlaps with communications, biology, and medical engineering. Entropy is a measure of uncertainty in the set of information. In this study, for each gene and its exons sets, the entropy was calculated in orders one to four. Based on the relative entropy of genes and exons, Kullback-Leibler divergence was calculated. After obtaining the Kullback-Leibler distance for genes and exons sets, the results were entered as input into 7 clustering algorithms: single, complete, average, weighted, centroid, median, and K-means. To aggregate the results of clustering, the AdaBoost algorithm was used. Finally, the results of the AdaBoost algorithm were investigated by GeneMANIA prediction server to explore the results from gene annotation point of view. All calculations were performed using the MATLAB Engineering Software (2015). Following our findings on investigating the results of genes metabolic pathways based on the gene annotations, it was revealed that our proposed clustering method yielded correct, logical, and fast results. This method at the same that had not had the disadvantages of aligning allowed the genes with actual length and content to be considered and also did not require high memory for large-length sequences. We believe that the performance of the proposed method could be used with other competitive gene clustering methods to group biologically relevant set of genes. Also, the proposed method can be seen as a predictive method for those genes bearing up weak genomic annotations.

Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles

Background. Precisely predicting cancer is crucial for cancer treatment. Gene expression profiles make it possible to analyze patterns between genes and cancers on the genome-wide scale. Gene expression data analysis, however, is confronted with enormous challenges for its characteristics, such as high dimensionality, small sample size, and low Signal-to-Noise Ratio. Results. This paper proposes a method, termed RS_SVM, to predict gene expression profiles via aggregating SVM trained on random subspaces. After choosing gene features through statistical analysis, RS_SVM randomly selects feature subsets to yield random subspaces and training SVM classifiers accordingly and then aggregates SVM classifiers to capture the advantage of ensemble learning. Experiments on eight real gene expression datasets are performed to validate the RS_SVM method. Experimental results show that RS_SVM achieved better classification accuracy and generalization performance in contrast with single SVM, K-nearest neighbor, decision tree, Bagging, AdaBoost, and the state-of-the-art methods. Experiments also explored the effect of subspace size on prediction performance. Conclusions. The proposed RS_SVM method yielded superior performance in analyzing gene expression profiles, which demonstrates that RS_SVM provides a good channel for such biological data.

CD109 plays a role in osteoclastogenesis

Osteoclasts are large multinucleated cells that arise from the fusion of cells from the monocyte/macrophage lineage. Osteoclastogenesis is mediated by macrophage colony-stimulating factor (M-CSF) and receptor activator of nuclear factor-kB ligand (RANKL) and involves a complex multistep process that requires numerous other elements, many of which remain undefined. The primary aim of this project was to identify novel factors which regulate osteoclastogenesis. To carry out this investigation, microarray analysis was performed comparing two pre-osteoclast cell lines generated from RAW264.7 macrophages: one that has the capacity to fuse forming large multinucleated cells and one that does not fuse. It was found that CD109 was up-regulated by>17-fold in the osteoclast forming cell line when compared to the cell line that does not fuse, at day 2 of the differentiation process. Results obtained with microarray were confirmed by RT-qPCR and Western blot analyses in the two cell lines, in the parental RAW264.7 cell line, as well as primary murine monocytes from bone marrow. A significant increase of CD109 mRNA and protein expression during osteoclastogenesis occurred in all tested cell types. In order to characterize the role of CD109 in osteoclastogenesis, CD109 stable knockdown cell lines were established and fusion of osteoclast precursors into osteoclasts was assessed. It was found that CD109 knockdown cell lines were less capable of forming large multinucleated osteoclasts. It has been shown here that CD109 is expressed in monocytes undergoing RANKL-induced osteoclastogenesis. Moreover, when CD109 expression is suppressed in vitro, osteoclast formation decreases. This suggests that CD109 might be an important regulator of osteoclastogenesis. Further research is needed in order to characterize the role played by CD109 in regulation of osteoclast differentiation.