The vast potential of the genomic insight offered by microarray technologies has led to their widespread use since they were introduced a decade ago. Application areas include gene function discovery, disease diagnosis, and inferring regulatory networks. Microarray experiments enable large-scale, high-throughput investigations of gene activity and have thus provided the data analyst with a distinctive, high-dimensional field of study. Many questions in this field relate to finding subgroups of data profiles which are very similar. A popular type of exploratory tool for finding subgroups is cluster analysis, and many different flavors of algorithms have been used and indeed tailored for microarray data. Cluster analysis, however, implies a partitioning of the entire data set, and this does not always match the objective. Sometimes pattern discovery or bump hunting tools are more appropriate. This paper reviews these various tools for finding interesting subgroups.

David J. Hand

Nicholas A. Heard

Finding Groups in Gene Expression Data

Clustering proteomics data is a challenging problem for any traditional clustering algorithm. Usually, the number of samples is largely smaller than the number of protein peaks. The use of a clustering algorithm which does not take into consideration the number of features of variables (here the number of peaks) is needed. An innovative hierarchical clustering algorithm may be a good approach. We propose here a new dissimilarity measure for the hierarchical clustering combined with a functional data analysis. We present a specific application of functional data analysis (FDA) to a high-throughput proteomics study. The high performance of the proposed algorithm is compared to two popular dissimilarity measures in the clustering of normal and human T-cell leukemia virus type <mml:math alttext="$1$" id="E1" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>1</mml:mn></mml:math> (HTLV-<mml:math alttext="$1$" id="E2" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>1</mml:mn></mml:math>)-infected patients samples.

Halima Bensmail

Buddana Aruna

O. John Semmes

Abdelali Haoudi

Functional Clustering Algorithm for High-Dimensional Proteomics Data

One important feature of the gene expression data is that the number of genes <mml:math alttext="$M$" id="E1" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>M</mml:mi></mml:math> far exceeds the number of samples <mml:math alttext="$N$" id="E2" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math>. Standard statistical methods do not work well when <mml:math alttext="$N &#x3C; M$" id="E3" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>N</mml:mi><mml:mo>&#x3C;</mml:mo><mml:mi>M</mml:mi></mml:mrow></mml:math>. Development of new methodologies or modification of existing methodologies is needed for the analysis of the microarray data. In this paper, we propose a novel analysis procedure for classifying the gene expression data. This procedure involves dimension reduction using kernel principal component analysis (KPCA) and classification with logistic regression (discrimination). KPCA is a generalization and nonlinear version of principal component analysis. The proposed algorithm was applied to five different gene expression datasets involving human tumor samples. Comparison with other popular classification methods such as support vector machines and neural networks shows that our algorithm is very promising in classifying gene expression data.

Zhenqiu Liu

Dechang Chen

Gene Expression Data Classification With Kernel Principal Component Analysis

Halima  Bensmail

Abdelali  Haoudi

Data Mining in Genomics and Proteomics

In a recently proposed contextual alignment model, efficient algorithms exist for global and local pairwise alignment of protein sequences. Preliminary results obtained for biological data are very promising. Our main motivation was to adopt the idea of context dependency to the multiple-alignment setting. To this aim the relaxation of the model was developed (we call this new model averaged contextual alignment) and a new family of amino acids substitution matrices are constructed. In this paper we present a contextual multiple-alignment algorithm and report the outcomes of experiments performed for the BAliBASE test set. The contextual approach turned out to give much better results for the set of sequences containing orphan genes.

Anna Gambin

Rafa&#x142; Otto

Contextual Multiple Sequence Alignment

A common practice of metanalysis is combining the results of numerous studies on the effects of a risk factor on a disease outcome.  If several of these composite relative risks are estimated from the medical literature for a specific disease, they cannot be combined in a multivariate risk model, as is often done in individual studies, because methods are not available to overcome the issues of risk factor colinearity and heterogeneity of the different cohorts.  We propose a solution to these problems for general linear regression of continuous outcomes using a simple example of combining two independent variables from two sources in estimating a joint outcome. We demonstrate that when
explicitly modifying the underlying data characteristics (correlation coefficients, standard deviations, and univariate
betas) over a wide range, the predicted outcomes remain reasonable estimates of empirically derived outcomes (gold standard). This method shows the most promise in situations where the primary interest is in generating predicted values as when identifying a high-risk group of individuals. The resulting partial regression coefficients are less robust than the predicted values.

Greg Samsa

Guizhou Hu

Martin Root

Combining Information From Multiple Data Sources to Create Multivariable Risk Models: Illustration and Preliminary Assessment of a New Method

Gene expression microarray data can be used for the assembly of genetic coexpression network graphs. Using mRNA samples obtained from recombinant inbred Mus musculus strains, it is possible to integrate allelic variation with molecular and higher-order phenotypes. The depth of quantitative genetic analysis of microarray data can be vastly enhanced utilizing this mouse resource in combination with powerful computational
algorithms, platforms, and data repositories. The resulting network graphs transect many levels of biological scale. This
approach is illustrated with the extraction of cliques of putatively co-regulated genes and their annotation using gene
ontology analysis and cis-regulatory element discovery. The causal basis for co-regulation is detected through the use of quantitative trait locus mapping.

Nicole E. Baldwin

Elissa J. Chesler

Stefan Kirov

Michael A. Langston

Jay R. Snoddy

Robert W. Williams

Bing Zhang

Computational, Integrative, and Comparative Methods for the Elucidation of Genetic Coexpression Networks

A sequence-to-structure library has been created based on the complete PDB database. The tetrapeptide was selected as a unit representing a well-defined structural motif. Seven structural forms were introduced for structure classification. The early-stage folding conformations were used as the objects for structure analysis and classification. The degree of determinability was estimated for the sequence-to-structure and structure-to-sequence relations. Probability calculus and informational entropy were applied for quantitative estimation of the mutual relation between them. The structural motifs representing different forms of loops and bends were found to
favor particular sequences in structure-to-sequence analysis.

Micha&#x142; Brylinski

Leszek Konieczny

Patryk Czerwonko

Wiktor Jurkowski

Irena Roterman

Early-Stage Folding in Proteins (In Silico) Sequence-to-Structure Relation

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Data Mining in Genomics and Proteomics

Data Mining in Genomics and Proteomics

Articles

Finding Groups in Gene Expression Data

Functional Clustering Algorithm for High-Dimensional Proteomics Data

Gene Expression Data Classification With Kernel Principal Component Analysis

Data Mining in Genomics and Proteomics

Contextual Multiple Sequence Alignment

Combining Information From Multiple Data Sources to Create Multivariable Risk Models: Illustration and Preliminary Assessment of a New Method

Computational, Integrative, and Comparative Methods for the Elucidation of Genetic Coexpression Networks

Early-Stage Folding in Proteins (In Silico) Sequence-to-Structure Relation