Integrated Use of Statistical-Based Approaches and Computational Intelligence Techniques for Tumors Classification Using Microarray

A reliable and precise classification of tumors is essential for successful diagnosis and treatment of cancer. cDNA microarrays and high-density oligonucleotide chips are novel biotechnologies increasingly used in cancer research. By allowing the monitoring of expression levels in cells for thousands of genes simultaneously, microarray experiments may lead to a more complete understanding of the molecular variations among tumors and hence to a finer and more informative classification. The ability to successfully distinguish between tumor classes (already known or yet to be discovered) using gene expression data is an important aspect of this novel approach to cancer classification. This article compares the performance of different discrimination methods for the classification of tumors based on gene expression data. The methods include nearest-neighbor classifiers, linear discriminant analysis, and classification trees. Recent machine learning approaches, such as bagging and boosting, are also considered. The discrimination methods are applied to datasets from three recently published cancer gene expression studies.

Deoxyribonucleic acid (DNA) microarrays are part of a promising class of biotechnologies that allow the simultaneous monitoring of expression levels in cells for thousands of genes. One of important issues in microarray experiments is the classification of biological samples and predicting clinical or other outcomes using gene expression data. A closely related issue is the identification of marker genes that have good predictive power for an outcome of interest. Although classification is not a new subject in the statistical literature, the large number of genes with relatively small sample size generated by microarray experiments raises new computational challenges. In this study, the gene expressions of breast cancer tumors are investigated and the performance of several popular classification methods, including decision tree, logistic regression, linear discriminant analysis, and k-nearest neighbor are compared. The results show that certain genes are significantly differentially expressed across groups of patients, and k-nearest neighbor method achieves better performance in class prediction than the other classification methods.

The advent of the so-called cDNA microarrays has offered the first possibility to obtain a global understanding of biological processes in living organisms by simultaneous readouts of tens of thousands of genes. Initial experiments suggest that genes with similar function have similar expression patterns in microarray experiments. Until now, most approaches to computational analysis of gene expressions have used unsupervised learning. Although in some cases unsupervised methods may be suficient, the complexity of the biological processes is so high that it is unlikely that purely syntactical analyses are capable of fully exploiting the richness of the microarray data. In addition, it seems natural to re-use the existing biological (background) knowledge. In this paper, we present some elements of a methodology for knowledge discovery from microarray experiments. Two source of bio-medical knowledge are used: Ashburner’s gene ontology and our own literature-derived network of gene-gene relations obtained by analysing Medline citation records. Predictive models can be induced and their classification quality validated through the ROC/AUC analysis and applied to provide hypotheses regarding the function of unclassified genes. The methodology has been so far tested on publicly available gene expression data and its results evaluated by molecular biologists and medical researchers.KeywordsKnowledge DiscoverySqualene EpoxidaseComputational Molecular BiologyNorwegian Radium HospitalUniversal AcademyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

A proposal for direct-ordering gene expression data by self-organising maps

Sequence databases and microarrays as tools for identifying prostate cancer biomarkers

The real-time system of the JT-60 has a cascade structure with the major and the minor control loops. The multi-variable feedback control is adopted in the major control loop in order to adjust non-linear effects which will appear in the high-/beta/region. By applying the concept of 'phase shift control' to the control program of the major control loop, a flexible tokamak discharge control is expected to be performed with taking account of machine protection.

Gene expression data that is being used to gather information from tissue samples is expected to significantly improve the development of efficient tumor diagnosis. For more accurate classification of tumor, extracting discriminant components from thousands of genes is an important problem which becomes challenging task due to the large number of genes and small sample size. We propose a novel approach which combines the revised feature score criterion with independent component analysis that has been developing recently to further improve the classification performance of gene expression data based on support vector machines. Two sets of gene expression data (colon tumor dataset and leukemia dataset) are examined to confirm that the proposed approach can extract a small quantity of independent components which drastically reduce the dimensionality of the original gene expression data when retaining higher recognition rate. For example, 100% cross-validation accuracy has been achieved with only extracting 2 or 3 independent components from leukemia dataset in our experiments.

DNA microarray technology allows to monitor simultaneously the expression levels of thousands of genes during important biological processes and across collections of related experiments. Clustering and classification techniques have proved to be helpful to understand gene function, gene regulation, and cellular processes. However the conventional proximity measures between genes expression data, used for clustering or classification purpose, do not fit gene expression specifications as they are based on the closeness of the expression magnitudes regardless of the overall gene expression profile (shape). We propose in this paper an adaptive dissimilarity index which would cover both values and behavior proximity. The effectiveness of the adaptive dissimilarity index is illustrated through a classification process for identification of genes cell cycle phases.KeywordsCell Cycle PhaseClassical CorrelationRand IndexPrior ClassProximity MeasureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Microarrays are capable of determining the expression levels of thousands of genes simultaneously. One important application of gene expression data is classification of samples into categories. In combination with classification methods, this technology can be useful to support clinical management decisions for individual patients, e.g. in oncology. Standard statistic methodologies in classification or prediction do not work well when the number of variables p (genes) far too exceeds the number of samples n. So, modification of existing statistical methodologies or development of new methodologies is needed for the analysis of microarray data. This paper proposes a new method for tumor classification using gene expression data. In this method, we first employ independent component analysis to model the gene expression data, then apply optimal scoring algorithm to classify them. Further speaking, this approach can first make full use of the high-order statistical information contained in the gene expression data. Second, this approach also employs regularized regression models to handle the situation of large numbers of correlated predictor variables. Finally, the predictive models are developed for classifying tumors based on the entire gene expression profile. To show the validity of the proposed method, we apply it to classify four DNA microarray datasets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible. Matlab scripts are available on request.

Gene expression based cancer classification

BackgroundIn microarray gene expression profiling experiments, differentially expressed genes (DEGs) are detected from among tens of thousands of genes on an array using statistical tests. It is important to control the number of false positives or errors that are present in the resultant DEG list. To date, more than 20 different multiple test methods have been reported that compute overall Type I error rates in microarray experiments. However, these methods share the following dilemma: they have low power in cases where only a small number of DEGs exist among a large number of total genes on the array.ResultsThis study contrasts parallel multiplicity of objectively related tests against the traditional simultaneousness of subjectively related tests and proposes a new assessment called the Error Discovery Rate (EDR) for evaluating multiple test comparisons in microarray experiments. Parallel multiple tests use only the negative genes that parallel the positive genes to control the error rate; while simultaneous multiple tests use the total unchanged gene number for error estimates. Here, we demonstrate that the EDR method exhibits improved performance over other methods in specificity and sensitivity in testing expression data sets with sequence digital expression confirmation, in examining simulation data, as well as for three experimental data sets that vary in the proportion of DEGs. The EDR method overcomes a common problem of previous multiple test procedures, namely that the Type I error rate detection power is low when the total gene number used is large but the DEG number is small.ConclusionsMicroarrays are extensively used to address many research questions. However, there is potential to improve the sensitivity and specificity of microarray data analysis by developing improved multiple test comparisons. This study proposes a new view of multiplicity in microarray experiments and the EDR provides an alternative multiple test method for Type I error control in microarray experiments.

BackgroundClustering is an important analysis performed on microarray gene expression data since it groups genes which have similar expression patterns and enables the exploration of unknown gene functions. Microarray experiments are associated with many sources of experimental and biological variation and the resulting gene expression data are therefore very noisy. Many heuristic and model-based clustering approaches have been developed to cluster this noisy data. However, few of them include consideration of probe-level measurement error which provides rich information about technical variability.ResultsWe augment a standard model-based clustering method to incorporate probe-level measurement error. Using probe-level measurements from a recently developed Affymetrix probe-level model, multi-mgMOS, we include the probe-level measurement error directly into the standard Gaussian mixture model. Our augmented model is shown to provide improved clustering performance on simulated datasets and a real mouse time-course dataset.ConclusionThe performance of model-based clustering of gene expression data is improved by including probe-level measurement error and more biologically meaningful clustering results are obtained.

Gene expression data composed of thousands of genes play an important role in classification platforms and disease diagnosis. Hence, it is vital to select a small subset of salient features over a large number of gene expression data. Lately, many researchers devote themselves to feature selection using diverse computational intelligence methods. However, in the progress of selecting informative genes, many computational methods face difficulties in selecting small subsets for cancer classification due to the huge number of genes (high dimension) compared to the small number of samples, noisy genes, and irrelevant genes. In this paper, we propose a new hybrid algorithm HICATS incorporating imperialist competition algorithm (ICA) which performs global search and tabu search (TS) that conducts fine-tuned search. In order to verify the performance of the proposed algorithm HICATS, we have tested it on 10 well-known benchmark gene expression classification datasets with dimensions varying from 2308 to 12600. The performance of our proposed method proved to be superior to other related works including the conventional version of binary optimization algorithm in terms of classification accuracy and the number of selected genes.

Biological procedures within cells are carried out by genes that can be studied using microarray technology. Gene expression data from a cell is stored and features of each cell are described by mercury. Microarray data is one of the latest breakthroughs used for selecting relevant biological information from thousands of genes simultaneously expressed in a distinct cell. Understanding the level of gene expression in a cell can provide valuable information in terms of identifying viral infection and determining cancer susceptibility. In a sample tissue, many genes are expressed. In that most of the genes are unrelated or irrelevant for clinical diagnosis. An accurate classification of diseases and phenotypes is selecting a small set of genes from thousands of genes. This paper concentrates on common gene expression classification techniques with MapReduce framework and related methods to find differentially expressed genes and select the significant genes in the specific gene expression.

Development of public toxicogenomics software for microarray data management and analysis