Gene expression databases contain a wealth of information, but current data mining tools are limited in their speed and effectiveness in extracting meaningful biological knowledge from them. Online analytical processing (OLAP) can be used as a supplement to cluster analysis for fast and effective data mining of gene expression databases. We used Analysis Services 2000, a product that ships with SQLServer2000, to construct an OLAP cube that was used to mine a time series experiment designed to identify genes associated with resistance of soybean to the
soybean cyst nematode, a devastating pest of soybean. The data for these experiments is stored in the soybean genomics and microarray database (SGMD). A number of candidate resistance genes and pathways were found. Compared to traditional cluster analysis of gene expression data, OLAP was more effective and faster in finding biologically meaningful information. OLAP is available from a number of vendors and can work with any relational database management system through OLE DB.

Nadim W. Alkharouf

D. Curtis Jamison

Benjamin F. Matthews

Online Analytical Processing (OLAP): A Fast and Effective Data Mining Tool for Gene Expression Databases

Gene selection is an important issue in analyzing multiclass microarray data. Among many proposed selection methods, the traditional ANOVA F test statistic has been employed to identify informative genes for both class prediction (classification) and discovery problems. However, the F test statistic assumes an equal variance. This assumption may not be realistic for gene expression data. This paper explores other alternative test statistics which can handle heterogeneity of the variances. We study five such test statistics, which include Brown-Forsythe test
statistic and Welch test statistic. Their performance is evaluated and compared with that of F statistic over different
classification methods applied to publicly available microarray datasets.

Dechang Chen

Zhenqiu Liu

Xiaobin Ma

Dong Hua

Selecting Genes by Test Statistics

The goal of proteomics is the complete characterization of all proteins. Efforts to characterize subcellular location have been limited to assigning proteins to general categories of organelles. We have previously designed numerical features to describe location patterns in microscope images and developed automated classifiers that distinguish major subcellular patterns with high accuracy (including patterns not distinguishable by visual examination). The results suggest the feasibility of automatically determining which proteins share a single location pattern in a given cell type. We describe an automated method that selects the best feature set to describe images for a given collection of proteins and constructs an effective partitioning of the proteins by location. An example for a limited protein set is presented. As additional data become available, this approach can produce for the first time an objective systematics for protein location and provide an important starting point for discovering sequence motifs that determine localization.

Xiang Chen

Robert F. Murphy

Objective Clustering of Proteins Based on Subcellular Location Patterns

Autopsy information on cardiovascular damage was investigated for pathologically confirmed Alzheimer disease (AD) patients (<mml:math alttext="$n=84$" id="E1" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn>84</mml:mn></mml:mrow></mml:math>) and non-AD control patients (<mml:math alttext="$n=60$" id="E2" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn>60</mml:mn></mml:mrow></mml:math>). The 51 relevant items were entered into a grade-of-membership model to describe vascular damage in AD. Five latent groups were identified &#x201C;I: early-onset AD,&#x201D; &#x201C;II: controls, cancer,&#x201D; &#x201C;III: controls, extensive atherosclerosis,&#x201D; &#x201C;IV: late-onset AD, male,&#x201D; and &#x201C;V:
late-onset AD, female.&#x201D; Expectedly, Groups IV and V had elevated APOE<mml:math alttext="$\epsilon 4$" id="E3" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>&#x3B5;</mml:mi><mml:mn>4</mml:mn></mml:mrow></mml:math> frequency. Unexpectedly, there was limited atherosclerosis and frequent myocardial valve and ventricular damage. The findings do not indicate a strong relationship between atherosclerosis and AD, although both are
associated with the APOE<mml:math alttext="$\epsilon 4$" id="E4" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>&#x3B5;</mml:mi><mml:mn>4</mml:mn></mml:mrow></mml:math>. Instead, autopsy findings of extensive atherosclerosis were associated with possible, not probable or definite AD, and premature death. They are consistent with the hypothesis that brain hypoperfusion contributes to dementia, possibly to AD pathogenesis, and raise the possibility that the APOE allele <mml:math alttext="$\epsilon 4$" id="E5" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>&#x3B5;</mml:mi><mml:mn>4</mml:mn></mml:mrow></mml:math> contributes directly to heart valve and myocardial damage.

Elizabeth H. Corder

John F. Ervin

Evelyn Lockhart

Mari H. Szymanski

Donald E. Schmechel

Christine M. Hulette

Cardiovascular Damage in Alzheimer Disease: Autopsy Findings From the Bryan ADRC

Most codon indices used today are based on highly biased nonrandom usage of codons in coding regions. The background of a coding or noncoding DNA sequence, however, is fairly random, and can be characterized as a random fractal. When a gene-finding algorithm incorporates multiple sources of information
about coding regions, it becomes more successful. It is thus highly desirable to develop new and efficient codon indices by simultaneously characterizing the fractal and periodic features of a DNA sequence. In this paper, we describe a novel way of achieving this goal. The efficiency of the new codon index is evaluated by studying all of the 16 yeast chromosomes. In particular, we show that the method automatically and correctly identifies which of the three
reading frames is the one that contains a gene.

Jianbo  Gao

Yan  Qi

Yinhe  Cao

Wen-wen  Tung

Protein Coding Sequence Identification by Simultaneously Characterizing the Periodic and Random Features of DNA Sequences

High-throughput gene expression technologies such as microarrays have been utilized in a variety of scientific applications. Most of the work has been done on assessing univariate associations between gene expression profiles with clinical outcome (variable selection) or on developing classification procedures with gene expression data (supervised learning). We consider a hybrid variable selection/classification approach that is based
on linear combinations of the gene expression profiles that maximize an accuracy measure summarized using the receiver operating characteristic curve.  Under a specific probability model, this leads to the consideration of linear discriminant functions. We incorporate an automated variable selection approach using LASSO. An equivalence between LASSO estimation with support vector machines allows for model fitting using standard software. We apply the proposed method to simulated data as well as data from a recently published prostate cancer study.

Debashis Ghosh

Arul M. Chinnaiyan

Classification and Selection of Biomarkers in Genomic Data Using LASSO

With about 200 000 phytochemicals in existence, identifying those of biomedical significance is a mammoth task. In the postgenomic era, relating metabolite fingerprints, abundances, and profiles to genotype is also a large task. Ion analysis using Fourier transformed ion cyclotron resonance mass spectrometry (FT-ICR-MS) may provide a high-throughput approach to measure genotype dependency of the inferred metabolome if reproducible techniques can be established. Ion profile inferred metabolite fingerprints are coproducts. We used FT-ICR-MS-derived ion analysis to examine gdhA (glutamate dehydrogenase (GDH; EC 1.4.1.1)) transgenic Nicotiana tabacum (tobacco) carrying out altered glutamate, amino acid, and carbon metabolisms, that fundamentally alter plant productivity. Cause and effect between gdhA expression, glutamate metabolism, and plant phenotypes was analyzed by 13NH4+ labeling of amino acid fractions, and by FT-ICR-MS analysis of metabolites. The gdhA transgenic plants increased 13N labeling of glutamate and glutamine significantly. FT-ICR-MS detected 2 012 ions reproducible in 2 to 4 ionization protocols. There were 283 ions in roots and 98 ions in leaves that appeared to significantly change abundance due to the measured GDH activity. About 58% percent of ions could not be used to infer a corresponding metabolite. From the 42% of ions that inferred known metabolites we found that certain amino acids, organic acids, and sugars increased and some fatty acids decreased. The transgene caused increased ammonium assimilation and detectable ion variation. Thirty-two compounds with biomedical significance were altered in abundance by GDH including 9 known carcinogens and 14 potential drugs. Therefore, the GDH transgene may lead to new uses for crops like tobacco.

R. Mungur

A. D. M. Glass

D. B. Goodenow

D. A. Lightfoot

Metabolite Fingerprinting in Transgenic Nicotiana tabacum Altered by the Escherichia coli Glutamate Dehydrogenase Gene

Structural features found in biomolecular networks that are absent in random networks produced by simple algorithms can provide insight into the function and evolution of cell regulatory networks. Here we analyze &#x201C;betweenness&#x201D; of network nodes, a graph theoretical centrality measure, in the yeast protein interaction network. Proteins that have high betweenness, but low connectivity (degree), were found to be abundant in the yeast proteome. This finding is not explained by algorithms proposed to explain the scale-free property of protein interaction networks, where low-connectivity proteins also have low betweenness. These data suggest the existence of some modular organization of the network, and that the high-betweenness, low-connectivity proteins may act as important links between these modules. We found that proteins with high betweenness are more likely to be essential and that evolutionary age of proteins is positively correlated with betweenness. By comparing different models of genome evolution that generate scale-free networks, we show that rewiring of interactions via mutation is an important factor in the production of such proteins. The evolutionary and functional significance of these observations are discussed.

Maliackal Poulo Joy

Amy Brock

Donald E. Ingber

Sui Huang

High-Betweenness Proteins in the Yeast Protein Interaction Network

We investigate the problems of multiclass cancer classification with gene selection from gene expression data. Two different constructed multiclass classifiers with gene selection are proposed, which are fuzzy support vector machine (FSVM) with gene selection and binary classification tree based on SVM with gene selection. Using F test and recursive feature elimination based on SVM as gene selection methods, binary classification tree based on
SVM with F test, binary classification tree based on SVM with recursive feature elimination based on SVM, and FSVM with recursive feature elimination based on SVM are tested in our experiments. To accelerate computation, preselecting the strongest genes is also used. The proposed techniques are applied to analyze
breast cancer data, small round blue-cell tumors, and acute leukemia data. Compared to existing multiclass cancer classifiers and binary classification tree based on SVM with F test or binary classification tree based on SVM with recursive feature elimination based on SVM mentioned in this paper, FSVM based on recursive feature elimination based on SVM can find most important genes that affect certain types of cancer with high recognition
accuracy.

Yong Mao

Xiaobo Zhou

Daoying Pi

Youxian Sun

Stephen T. C. Wong

Multiclass Cancer Classification by Using Fuzzy Support Vector Machine and Binary Decision Tree With Gene Selection

The etiological origin of type 2 diabetes mellitus (T2DM) has long been controversial. The body of literature related to T2DM is vast and varied in focus, making a broad epidemiological perspective
difficult, if not impossible. A data-mining approach was used to analyze all electronically available scientific literature, over 12 million Medline records, for &#x201C;objects&#x201D; such as genes, diseases, phenotypes, and chemical compounds linked to other objects within the T2DM literature but were not themselves within
the T2DM literature. The goal of this analysis was to conduct a comprehensive survey to identify novel factors implicated in the pathology of T2DM by statistically evaluating mutually shared associations. Surprisingly, epigenetic factors were among the highest statistical scores in this analysis, strongly implicating epigenetic changes within the body as causal factors in the pathogenesis of T2DM. Further analysis implicates adipocytes as the potential tissue of origin, and cytokines or cytokine-like genes as the dysregulated factor(s) responsible for the T2DM phenotype. The analysis provides a wealth of literature supporting this hypothesis, which&#x2014;if true&#x2014;represents an important paradigm shift for researchers studying the pathogenesis of T2DM.

Jonathan D. Wren

Harold R. Garner

Data-Mining Analysis Suggests an Epigenetic Pathogenesis for Type 2 Diabetes

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