Regulatory Genes Through Robust-SNR for Binary Classification Within Functional Genomics Experiments

The current study proposes a novel technique for feature selection by inculcating robustness in the conventional Signal to noise Ratio(SNR).The proposed method utilizes the robust measures of location i.e.,the“Median”as well as the measures of variation i.e.,“Median absolute deviation(MAD)and Interquartile range(IQR)”in the SNR.By this way,two independent robust signal-to-noise ratios have been proposed.The proposed method selects the most informative genes/features by combining the minimum subset of genes or features obtained via the greedy search approach with top-ranked genes selected through the robust signal-to-noise ratio(RSNR).The results obtained via the proposed method are compared with wellknown gene/feature selection methods on the basis of performance metric i.e.,classification error rate.A total of 5 gene expression datasets have been used in this study.Different subsets of informative genes are selected by the proposed and all the other methods included in the study,and their efficacy in terms of classification is investigated by using the classifier models such as support vector machine(SVM),Random forest(RF)and k-nearest neighbors(k-NN).The results of the analysis reveal that the proposed method(RSNR)produces minimum error rates than all the other competing feature selection methods in majority of the cases.For further assessment of the method,a detailed simulation study is also conducted.

Gene expression profile differences in high and low metastatic human ovarian cancer cell lines by gene chip

Objectives To study the difference between gene expressions of high (H0-8910PM) and low (HO-8910) metastatic human ovarian carcinoma cell lines and screen novel associated genes by cDNA microarray. Methods cDNA retro-transcribed from equal quantities of mRNA derived from high and low metastatic tumor cells or normal ovarian tissues were labeled with Cy5 and Cy3 fluorescein as probes. The mixed probe was hybridized with two pieces of BioDoor 4096 double dot human whole gene chip and scanned with a ScanArray 3000 laser scanner. The acquired image was analyzed by ImaGene 3.0 software. Results A total of 355 genes with expression levels more than 3 times larger were found by comparing the HO-8910 cell with normal ovarian epithelial cells. A total of 323 genes with expression levels more than 3 times larger in HO-8910PM cells compared to normal ovarian epithelium cells were also detected. A total of 165 genes whose expression levels were more than two times those of HO-8910PM cells compared to their mother cell line (HO-8910) were detected. Twenty-one genes with expression levels 】3 times were found from comparison of these two tumor cell lines.Conclusions cDNA microarray techniques are effective in screening differential gene expression between two human ovarian cancer cell lines (H0-8910PM; HO-8910) and normal ovarian epithelial cells. These genes may be related to the genesis and development of ovarian carcinoma. Analysis of the human ovarian cancer gene expression profile with cDNA microarray may help in gene diagnosis, treatment and prevention.