Gene Selection Procedure Research Articles

Computational selection of distinct class- and subclass-specific gene expression signatures

In this investigation we used statistical methods to select genes with expression profiles that partition classes and subclasses of biological samples. Gene expression data corresponding to liver samples from rats treated for 24 h with an enzyme inducer (phenobarbital) or a peroxisome proliferator (clofibrate, gemfibrozil or Wyeth 14,643) were subjected to a modified Z-score test to identify gene outliers and a binomial distribution to reduce the probability of detecting genes as differentially expressed by chance. Hierarchical clustering of 238 statistically valid differentially expressed genes partitioned class-specific gene expression signatures into groups that clustered samples exposed to the enzyme inducer or to peroxisome proliferators. Using analysis of variance (ANOVA) and linear discriminant analysis methods we identified single genes as well as coupled gene expression profiles that separated the phenobarbital from the peroxisome proliferator treated samples and discerned the fibrate (gemfibrozil and clofibrate) subclass of peroxisome proliferators. A comparison of genes ranked by ANOVA with genes assessed as significant by mixed linear models analysis [J. Comput. Biol. 8 (2001) 625] or ranked by information gain revealed good congruence with the top 10 genes from each statistical method in the contrast between phenobarbital and peroxisome proliferators expression profiles. We propose building upon a classification regimen comprised of analysis of replicate data, outlier diagnostics and gene selection procedures to utilize cDNA microarray data to categorize subclasses of samples exposed to pharmacologic agents.

Variable selection and pattern recognition with gene expression data generated by the microarray technology

Lack of adequate statistical methods for the analysis of microarray data remains the most critical deterrent to uncovering the true potential of these promising techniques in basic and translational biological studies. The popular practice of drawing important biological conclusions from just one replicate (slide) should be discouraged. In this paper, we discuss some modern trends in statistical analysis of microarray data with a special focus on statistical classification (pattern recognition) and variable selection. In addressing these issues we consider the utility of some distances between random vectors and their nonparametric estimates obtained from gene expression data. Performance of the proposed distances is tested by computer simulations and analysis of gene expression data on two different types of human leukemia. In experimental settings, the error rate is estimated by cross-validation, while a control sample is generated in computer simulation experiments aimed at testing the proposed gene selection procedures and associated classification rules.

A genetic algorithm for sequential part assignment for PCB assembly

We present a genetic algorithm for printed circuit board (PCB) assembly, which simultaneously solves the feeder assignment and component sequencing problems. The algorithm uses a unique gene selection procedure that increases the convergence rate without degrading the solution quality. We have compared the performance of our algorithm with existing approaches and have demonstrated improved performance. We have also implemented the algorithm on a Quad IIIc insertion machine for surface mount components. The resulting suboptimal assembly times estimated by the algorithm have been found to be close to the actual optimal values.

ALL/AML Cancer Classification by Gene Expression Data Using SVM and CSVM Approach

Cancer classification plays an important role in cancer treatment. There has been no general approach for this problem now. The tasks for cancer classification are of two aspects: identifying new cancer classes and assigning tumors to known classes, which are called class discovery and class prediction by Golub et al. [1]. From mathematical point of view, class discovery is a cluster analysis problem, while class prediction is usually called classification problem (we’ll use the later name to keep consist with pattern recognition literatures). Until now, cancer classification has been based primarily on morphological appearance of tumor [1]. This has serious limitations because of ambiguity. Golub et al. presented a new approach to cancer classification based on gene expression monitoring by DNA microarrays in [1]. They chose acute leukemia as a test case, and the target is to distinguish between ALL (acute lymphoblastic leukemia) and AML (acute myeloid leukemia), which is a typical cancer classification problem not well solved despite many years of efforts. This paper is a report of our work on the classification (prediction) part of this problem following their original work. Golub et al. adopted a feature selection (gene selection) procedure before classification. A metric was defined to evaluate the correlation of each gene to the classification. After some “good” genes were selected from all the 6817 genes, the classification is done by a weighted voting scheme. The classifier was trained on a 38-sample training set, and another 34-sample set was used for testing. With leave-one-out cross-validation on the training set with 50 selected genes, 36 out of 38 samples were correctly classified and 2 were rejected (no-call). The performance on the test set was that 29 samples out of 34 were correctly classified and the other 5 were rejected. If the classifier were compelled to give these 5 no-calls a prediction, the prediction would be wrong. Since the feature selection procedure is of single selection type, and the classification method is also an intuitive one, we believe that there is still much space for the performance to be improved. In our approach to the problem, we took all the genes for the classification (the selection problem will be discussed in another paper), and applied the support vector machine(SVM) method and one of its improved version CSVM as the classifier. Thanks to the better generalization ability of SVM and CSVM, much better performance was obtained.

Induction of an anti-viral response and 2′,5′-oligo a synthetase by interferon in several thymidine kinase-deficient cell lines

It has previously been shown that a strain of thymidine kinase (tk)-deficient mouse L-929 cells was unable to respond to murine β-interferon by induction of an anti-viral state and synthesis of double-stranded, RNA-dependent enzymes. Sensitivity to interferon can be restored by introducing into the cells a segment of Herpes simplex virus DNA containing the viral tk gene. It is shown here that not all Ltk(−) cell strains are resistant to interferon, suggesting that expression of a tk gene is not a prerequisite for response to interferon. Introduction of various genes into the resistant Ltk(−) strain, either alone or together with DNA containing the Herpes virus tk gene, leads to restoration of interferon sensitivity only when tk-containing DNA is inserted, showing that the activation of interferon responsiveness is not an artifact of the gene transfer, selection, and cloning procedures. The results imply that a component of the Herpes virus DNA introduced into the cells is able to activate interferon sensitivity.