A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data

Background: Feature selection techniques are critical to the analysis of high dimensional datasets. This is especially true in gene selection from microarray data which are commonly with extremely high feature-to-sample ratio. In addition to the essential objectives such as to reduce data noise, to reduce data redundancy, to improve sample classification accuracy, and to improve model generalization property, feature selection also helps biologists to focus on the selected genes to further validate their biological hypotheses.
Results: In this paper we describe an improved hybrid system for gene selection. It is based on a recently proposed genetic ensemble (GE) system. To enhance the generalization property of the selected genes or gene subsets and to overcome the overfitting problem of the GE system, we devised a mapping strategy to fuse the goodness information of each gene provided by multiple filtering algorithms. This information is then used for initialization and mutation operation of the genetic ensemble system.
Conclusion: We used four benchmark microarray datasets (including both binary-class and multi-class classification problems) for concept proving and model evaluation. The experimental results indicate that the proposed multi-filter enhanced genetic ensemble (MF-GE) system is able to improve sample classification accuracy, generate more compact gene subset, and converge to the selection results more quickly. The MF-GE system is very flexible as various combinations of multiple filters and classifiers can be incorporated based on the data characteristics and the user preferences.

Simplifying gene expression microarray comparative analysis.

Gene Expression Comparative Analysis allows bioinformatics researchers to discover the conserved or specific functional regulation of genes. This is achieved through comparisons between quantitative gene expression measurements obtained in different species on different platforms to address a particular biological system. Comparisons are made more difficult due to the need to map orthologous genes between species, pre-processing of data (normalization) and post-analysis (statistical and correlation analysis). In this paper we introduce a web-based software package called EXP-PAC which provides on line interfaces for database construction and query of data, and makes use of a high performance computing platform of computer clusters to run gene sequence mapping and normalization methods in parallel. Thus, EXP-PAC facilitates the integration of gene expression data for comparative analysis and the online sharing, retrieval and visualization of complex multi-specific and multi-platform gene expression results.

A novel aggregate gene selection method for microarray data classification

This paper introduces a novel method for gene selection based on a modification of analytic hierarchy process (AHP). The modified AHP (MAHP) is able to deal with quantitative factors that are statistics of five individual gene ranking methods: two-sample t-test, entropy test, receiver operating characteristic curve, Wilcoxon test, and signal to noise ratio. The most prominent discriminant genes serve as inputs to a range of classifiers including linear discriminant analysis, k-nearest neighbors, probabilistic neural network, support vector machine, and multilayer perceptron. Gene subsets selected by MAHP are compared with those of four competing approaches: information gain, symmetrical uncertainty, Bhattacharyya distance and ReliefF. Four benchmark microarray datasets: diffuse large B-cell lymphoma, leukemia cancer, prostate and colon are utilized for experiments. As the number of samples in microarray data datasets are limited, the leave one out cross validation strategy is applied rather than the traditional cross validation. Experimental results demonstrate the significant dominance of the proposed MAHP against the competing methods in terms of both accuracy and stability. With a benefit of inexpensive computational cost, MAHP is useful for cancer diagnosis using DNA gene expression profiles in the real clinical practice.

Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice.

Comparative microarray analysis identifies commonalities in neuronal injury: evidence for oxidative stress, dysfunction of calcium signalling, and inhibition of autophagy-lysosomal pathway

Mitochondrial dysfunction, ubiquitin-proteasomal system impairment and excitotoxicity occur during the injury and death of neurons in neurodegenerative conditions. The aim of this work was to elucidate the cellular mechanisms that are universally altered by these conditions. Through overlapping expression profiles of rotenone-, lactacystin- and N-methyl-D-aspartate-treated cortical neurons, we have identified three affected biological processes that are commonly affected; oxidative stress, dysfunction of calcium signalling and inhibition of the autophagic-lysosomal pathway. These data provides many opportunities for therapeutic intervention in neurodegenerative conditions, where mitochondrial dysfunction, proteasomal inhibition and excitotoxicity are evident.

Lung cancer prediction from microarray data by gene expression programming

Lung cancer is a leading cause of cancer-related death worldwide. The early diagnosis of cancer has demonstrated to be greatly helpful for curing the disease effectively. Microarray technology provides a promising approach of exploiting gene profiles for cancer diagnosis. In this study, the authors propose a gene expression programming (GEP)-based model to predict lung cancer from microarray data. The authors use two gene selection methods to extract the significant lung cancer related genes, and accordingly propose different GEP-based prediction models. Prediction performance evaluations and comparisons between the authors' GEP models and three representative machine learning methods, support vector machine, multi-layer perceptron and radial basis function neural network, were conducted thoroughly on real microarray lung cancer datasets. Reliability was assessed by the cross-data set validation. The experimental results show that the GEP model using fewer feature genes outperformed other models in terms of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. It is concluded that GEP model is a better solution to lung cancer prediction problems.

A custom-built insulin resistance gene chip

Objectives/Aim—Microarray (gene chip) technology offers a powerful new tool for analyzing the expression of large numbers of genes in many experimental samples. The aim of this study was to design, construct, and use a gene chip to measure the expression levels of key genes in metabolic pathways related to insulin resistance.
Methods—We selected genes that were implicated in the development of insulin resistance, including genes involved in insulin signaling; glucose uptake, oxidation, and storage; fat uptake, oxidation, and storage; cytoskeletal components; and transcription factors. The key regulatory genes in the pathways were identified, along with other recently identified candidate genes such as calpain-10. A total of 242 selected genes (including 32 internal control elements) were sequence-verified, purified, and arrayed on aldehyde-coated slides.
Results—Where more than 1 clone containing the gene of interest was available, we chose those containing the genes in the 5' orientation and an insert size of around 1.5 kb. Of the 262 clones purchased, 56 (21%) were found to contain sequences other than those expected. In addition, 2 (1%) did not grow under standard conditions and were assumed to be nonviable. In these cases, alternate clones containing the gene of interest were chosen as described above. The current version of the Insulin Resistance Gene Chip contains 210 genes of interest, plus 48 control elements. A full list of the genes is available at http://www.hbs.deakin.edu.au/mru/research/gene_chip_tech/genechip_three.htm/.
Conclusions—The human Insulin Resistance Gene Chip that we have constructed will be a very useful tool for investigating variation in the expression of genes relevant to insulin resistance under various experimental conditions. Initially, the gene chip will be used in studies such as exercise interventions, fasting, euglycemic-hyperinsulinemic clamps, and administration of antidiabetic agents

Identification of novel genes expressed during rhabdomyosarcoma differentiation using cDNA microarrays

Rhabdomyosarcomas (RMS) are highly aggressive tumors that are thought to arise as a consequence of the regulatory disruption of the growth and differentiation of skeletal muscle progenitor cells. Normal myogenesis is characterized by the expression of the myogenic regulatory factor gene family but, despite their expression in RMS, these tumor cells fail to complete the latter stages of myogenesis. The RMS cell line RD-A was treated with 12-O-tetradecanoylphorbol-13-acetate to induce differentiation and cultured for 10 days. RNA was extracted on days 1, 3, 6, 8 and 10. A human skeletal muscle cDNA microarray was developed and used to analyze the global gene expression of RMS tumors over the time-course of differentiation. As a comparison, the genes identified were subsequently examined during the differentiated primary human skeletal muscle cultures. Prothymosin alpha (PTMA), and translocase of inner mitochondrial membrane 10 (Tim10), two genes not previously implicated in RMS, showed reduced expression during differentiation. Marked differences in the expression of PTMA and Tim10 were observed during the differentiation of human primary skeletal muscle cells. These results identify several new genes with potential roles in the myogenic arrest present in rhabdomyosarcoma. PTMA expression in RMS biopsy samples might prove to be an effective diagnostic marker for this disease.

Finding rule groups to classify high dimensional gene expression datasets

Microarray data provides quantitative information about the transcription profile of cells. To analyze microarray datasets, methodology of machine learning has increasingly attracted bioinformatics researchers. Some approaches of machine learning are widely used to classify and mine biological datasets. However, many gene expression datasets are extremely high dimensionality, traditional machine learning methods can not be applied effectively and efficiently. This paper proposes a robust algorithm to find out rule groups to classify gene expression datasets. Unlike the most classification algorithms, which select dimensions (genes) heuristically to form rules groups to identify classes such as cancerous and normal tissues, our algorithm guarantees finding out best-k dimensions (genes), which are most discriminative to classify samples in different classes, to form rule groups for the classification of expression datasets. Our experiments show that the rule groups obtained by our algorithm have higher accuracy than that of other classification approaches

CIDB: Chlamydia Interactive Database for cross-querying genomics, transcriptomics and proteomics data

Chlamydiae are important pathogens of humans, birds and a wide range of animals. They are a unique group of bacteria, characterized by their developmental cycle. Chlamydia has been difficult to study because of their obligate intracellular growth habit and lack of a genetic transformation system. However, the past 5 years has seen the full genome sequencing of seven strains of Chlamydia and a rapid expansion of genomic, transcriptomic (RT-PCR, microarray) and proteomic analysis of these pathogens. The Chlamydia Interactive Database (CIDB) described here is the first database of its type that holds genomic, RT-PCR, microarray and proteomics data sets that can be cross-queried by researchers for patterns in the data. Combining the data of many research groups into a single database and cross-querying from different perspectives should enhance our understanding of the complex cell biology of these pathogens. The database is available at: http://www3.it.deakin.edu.au:8080/CIDB/.