Multi-class cancer classification using multinomial probit regression with Bayesian gene selection.

We consider the problems of multi-class cancer classification from gene expression data. After discussing the multinomial probit regression model with Bayesian gene selection, we propose two Bayesian gene selection schemes: one employs different strongest genes for different probit regressions; the other employs the same strongest genes for all regressions. Some fast implementation issues for Bayesian gene selection are discussed, including preselection of the strongest genes and recursive computation of the estimation errors using QR decomposition.

What should be expected from feature selection in small-sample settings.

Motivation: High-throughput technologies for rapid measurement of vast numbers of biological variables offer the potential for highly discriminatory diagnosis and prognosis; however, high dimensionality together with small samples creates the need for feature selection, while at the same time making feature-selection algorithms less reliable. Feature selection must typically be carried out from among thousands of gene-expression features and in the context of a small sample (small number of microarrays). Two basic questions arise: (1) Can one expect feature selection to yield a feature set whose error is close to that of an optimal feature set? (2) If a good feature set is not found, should it be expected that good feature sets do not exist?

Results: The two questions translate quantitatively into questions concerning conditional expectation.

Inferring gene regulatory networks from time series data using the minimum description length principle.

Motivation: A central question in reverse engineering of genetic networks consists in determining the dependencies and regulating relationships among genes. This paper addresses the problem of inferring genetic regulatory networks from time-series gene-expression profiles. By adopting a probabilistic modeling framework compatible with the family of models represented by dynamic Bayesian networks and probabilistic Boolean networks, this paper proposes a network inference algorithm to recover not only the direct gene connectivity but also the regulating orientations.

Role of endogenous XAP2 protein on the localization and nucleocytoplasmic shuttling of the endogenous mouse Ahb-1 receptor in the presence and absence of ligand.

Studies using transient expression systems have implicated the hepatitis B virus X-associated protein (XAP2) in the control of aryl hydrocarbon receptor (AHR) stability and subcellular location. Studies were performed in Hepa-1 cells to evaluate these functions of XAP2 on the mouse Ahb-1 receptor under endogenous stoichiometry. The Ahb-1 receptor is cytoplasmic, and it becomes predominantly nuclear after 30 to 60 min of ligand exposure with minimal degradation.

Noise-injected neural networks show promise for use on small-sample expression data.

Background: Overfitting the data is a salient issue for classifier design in small-sample settings. This is why selecting a classifier from a constrained family of classifiers, ones that do not possess the potential to too finely partition the feature space, is typically preferable. But overfitting is not merely a consequence of the classifier family; it is highly dependent on the classification rule used to design a classifier from the sample data.

Comparing automatic and manual image processing in FLARE assay analysis for colon carcinogenesis.

Measurement of the amount of oxidative damage to DNA is one tool that can be used to estimate the beneficial effect of diet on the prevention of colon carcinogenesis. The FLARE assay is a modification of the single-cell gel electrophoresis (Comet) assay, and provides a measure of the 8OHdG adduct in the cells. In this paper, we present two innovations to the existing methods of analysis.

Genetic test bed for feature selection.

Motivation: Given a large set of potential features, such as the set of all gene-expression values from a microarray, it is necessary to find a small subset with which to classify. The task of finding an optimal feature set of a given size is inherently combinatoric because to assure optimality all feature sets of a given size must be checked. Thus, numerous suboptimal feature-selection algorithms have been proposed.

Intervention in a family of Boolean networks.

Motivation: Intervention in a gene regulatory network is used to avoid undesirable states, such as those associated with a disease. Several types of intervention have been studied in the framework of a probabilistic Boolean network (PBN), which is a collection of Boolean networks in which the gene state vector transitions according to the rules of one of the constituent networks and where network choice is governed by a selection distribution. The theory of automatic control has been applied to find optimal strategies for manipulating external control variables that affect the transition probabilities to desirably affect dynamic evolution over a finite time horizon.

Circadian regulation of a viral gene promoter in live transgenic mice expressing firefly luciferase.

Purpose: This study was conducted to test for possible circadian control of viral infection in live animals using bioluminescence imaging of a firefly luciferase transgene.

Methods: Transgenic mice expressing the firefly luciferase gene under the control of the promoter and enhancer of the human cytomegalovirus major immediate-early gene (CMV::luc) were examined through whole-animal imaging. Mice were crossed with HRS/J hairless albino mice to improve imaging of deep structures.

Role of the carboxy-terminal transactivation domain and active transcription in the ligand-induced and ligand-independent degradation of the mouse Ahb-1 receptor.

To assess the importance of transactivation domains (TAD), DNA binding and transcription on the degradation of the AH receptor (AHR), Hepa-1 cells were pre-treated with actinomycin D (AD) or cycloheximide (CHX) and exposed to 2,3,7,8 tetrachlorodibenzo-p-dioxin (TCDD). AD or CHX did not affect nuclear localization or DNA binding of the AHR but inhibited ligand-induced degradation. In contrast, AD or CHX did not inhibit geldanamycin (GA) induced degradation of the AHR.

Generating Boolean networks with a prescribed attractor structure.

Motivation: Dynamical modeling of gene regulation via network models constitutes a key problem for genomics. The long-run characteristics of a dynamical system are critical and their determination is a primary aspect of system analysis. In the other direction, system synthesis involves constructing a network possessing a given set of properties.

Redefining the role of the endogenous XAP2 and C-terminal hsp70-interacting protein on the endogenous Ah receptors expressed in mouse and rat cell lines.

Studies using transient expression systems have implicated the XAP2 protein in the control of aryl hydrocarbon receptor (AHR) stability and subcellular location. Thus, studies were performed in cell lines that expressed endogenous rat or mouse Ah(b-1) (C57BL/6) or Ah(b-2) (C3H) AHRs with similar levels of endogenous XAP2. Unliganded rat and mouse Ah(b-2) receptor complexes associated with reduced levels of XAP2 and exhibited dynamic nucleocytoplasmic shuttling in comparison with Ah(b-1) receptors.

Boolean relationships among genes responsive to ionizing radiation in the NCI 60 ACDS.

Motivation: An early use of gene-expression data coming from microarrays was to discover non-linear multivariate intergene relationships. Pursuing this direction, the motivation for this paper is 2-fold: (1) to discover and elucidate multivariate logical predictive relations among gene expressions in a dataset arising from radiation studies using the NCI 60 Anti-Cancer Drug Screen (ACDS) cell lines; and (2) to demonstrate how these logical relations based on coarse quantization reflect corresponding relations in the continuous data.

Results: Using the coefficient of determination, a large number of logical relationships have been discovered among genes in the NCI 60 ACDS cell lines.

Optimal number of features as a function of sample size for various classification rules.

Motivation: Given the joint feature-label distribution, increasing the number of features always results in decreased classification error; however, this is not the case when a classifier is designed via a classification rule from sample data. Typically (but not always), for fixed sample size, the error of a designed classifier decreases and then increases as the number of features grows. The potential downside of using too many features is most critical for small samples, which are commonplace for gene-expression-based classifiers for phenotype discrimination.

Intervention in context-sensitive probabilistic Boolean networks.

Motivation: Intervention in a gene regulatory network is used to help it avoid undesirable states, such as those associated with a disease. Several types of intervention have been studied in the framework of a probabilistic Boolean network (PBN), which is essentially a finite collection of Boolean networks in which at any discrete time point the gene state vector transitions according to the rules of one of the constituent networks. For an instantaneously random PBN, the governing Boolean network is randomly chosen at each time point.

Superior feature-set ranking for small samples using bolstered error estimation.

Motivation: Ranking feature sets is a key issue for classification, for instance, phenotype classification based on gene expression. Since ranking is often based on error estimation, and error estimators suffer to differing degrees of imprecision in small-sample settings, it is important to choose a computationally feasible error estimator that yields good feature-set ranking.

Results: This paper examines the feature-ranking performance of several kinds of error estimators: resubstitution, cross-validation, bootstrap and bolstered error estimation.

Which is better for cDNA-microarray-based classification: ratios or direct intensities.

Motivation: There are two general methods for making gene-expression microarrays: one is to hybridize a single test set of labeled targets to the probe, and measure the background-subtracted intensity at each probe site; the other is to hybridize both a test and a reference set of differentially labeled targets to a single detector array, and measure the ratio of the background-subtracted intensities at each probe site. Which method is better depends on the variability in the cell system and the random factors resulting from the microarray technology. It also depends on the purpose for which the microarray is being used.

Chemopreventive n-3 polyunsaturated fatty acids reprogram genetic signatures during colon cancer initiation and progression in the rat.

The mechanisms by which n-3 polyunsaturated fatty acids (PUFAs) decrease colon tumor formation have not been fully elucidated. Examination of genes up- or down-regulated at various stages of tumor development via the monitoring of gene expression relationships will help to determine the biological processes ultimately responsible for the protective effects of n-3 PUFA. Therefore, using a 3 x 2 x 2 factorial design, we used Codelink DNA microarrays containing approximately 9000 genes to help decipher the global changes in colonocyte gene expression profiles in carcinogen-injected Sprague Dawley rats.

Influence of a combination of two tetrachlorobiphenyl congeners (PCB 47; PCB 77) on thyroid status, choline acetyltransferase (ChAT) activity, and short- and long-term memory in 30-day-old Sprague-Dawley rats.

The important role of thyroid hormones in growth and development, maintenance of body temperature, digestion, cardiac function, and normal brain development can be disrupted by environmental contaminants like polychlorinated biphenyls (PCB). Polychlorinated biphenyls are environmental contaminants that are widespread, persistent, lipophilic, and bioaccumulate through food webs, concentrating in adipose tissue. Placental and lactational PCB exposure of offspring causes metabolic and endocrine disruptions including hypothyroxinemia, spatial learning and memory deficits, neurochemical and neurobehavioral alterations, and reproductive problems.

How many samples are needed to build a classifier: a general sequential approach.

Motivation: The standard paradigm for a classifier design is to obtain a sample of feature-label pairs and then to apply a classification rule to derive a classifier from the sample data. Typically in laboratory situations the sample size is limited by cost, time or availability of sample material. Thus, an investigator may wish to consider a sequential approach in which there is a sufficient number of patients to train a classifier in order to make a sound decision for diagnosis while at the same time keeping the number of patients as small as possible to make the studies affordable.

Noise factor analysis for cDNA microarrays.

A microarray-image model is used that takes into account many factors, including spot morphology, signal strength, background fluorescent noise, and shape and surface degradation. The model yields synthetic images whose appearance and quality reflect that of real microarray images. The model is used to link noise factors to the fidelity of signal extraction with respect to a standard image-extraction algorithm.

A Bayesian connectivity-based approach to constructing probabilistic gene regulatory networks.

Motivation: We have hypothesized that the construction of transcriptional regulatory networks using a method that optimizes connectivity would lead to regulation consistent with biological expectations. A key expectation is that the hypothetical networks should produce a few, very strong attractors, highly similar to the original observations, mimicking biological state stability and determinism. Another central expectation is that, since it is expected that the biological control is distributed and mutually reinforcing, interpretation of the observations should lead to a very small number of connection schemes.

Gene clustering based on clusterwide mutual information.

Cluster analysis of gene-wide expression data from DNA microarray hybridization studies has proved to be a useful tool for identifying biologically relevant groupings of genes and constructing gene regulatory networks. The motivation for considering mutual information is its capacity to measure a general dependence among gene random variables. We propose a novel clustering strategy based on minimizing mutual information among gene clusters.

Unraveling gene-gene interactions regulated by ligands of the aryl hydrocarbon receptor.

The co-expression of genes coupled to additive probabilistic relationships was used to identify gene sets predictive of the complex biological interactions regulated by ligands of the aryl hydrocarbon receptor ((Italic)Ahr(/Italic)). To maximize the number of possible gene-gene combinations, data sets from murine embryonic kidney, fetal heart, and vascular smooth muscle cells challenged (Italic)in vitro(/Italic) with ligands of the (Italic)Ahr(/Italic) were used to create predictor/training data sets. Biologically relevant gene predictor sets were calculated for (Italic)Ahr(/Italic), cytochrome P450 1B1, insulin-like growth factor-binding protein-5, lysyl oxidase, and osteopontin.