Due to the recent progress of the DNA microarray technology, a large number of gene expression profile data are being produced. How to analyze gene expression data is an important topic in computational molecular biology. Several studies have been done using the Boolean network as a model of a genetic network. This paper proposes efficient algorithms for identifying Boolean networks of bounded indegree and related biological networks, where identification of a Boolean network can be formalized as a problem of identifying many Boolean functions simultaneously. For the identification of a Boolean network, an 1 time naive algorithm and a simple time algorithm are known, where denotes the number of nodes, denotes the number of examples, and denotes the maximum indegree. This paper presents an improved 2 3 time Monte-Carlo type randomized algorithm, where is the exponent of matrix multiplication (currently, 2 376). The algorithm is obtained by combining fast matrix multiplication with the randomized fingerprint function for string matching. Although the algorithm and its analysis are simple, the result is nontrivial and the technique can be applied to several related problems.

Summary: Visual programming offers an intuitive means of combining known analysis and visualization methods into powerful applications. The system presented here enables users who are not programmers to manage microarray and genomic data flow and to customize their analysis by combining common data analysis tools to fit their needs.

Microarray technology allows a scientist to study genome-wide patterns of gene expression. Thousands of individual genes are measured with relatively small number of replications which poses challenges to traditional statistical methods. In particular, the gene-specific estimators of variances are not reliable and gene-by-gene tests have low power. In this paper we propose a family of shrinkage estimators for variances raised to a fixed power. We derive optimal shrinkage parameters under both Stein and the squared loss functions. Our results show that the standard sample variance is inadmissible under either loss functions. We propose several estimators for the optimal shrinkage parameters and investigate their asymptotic properties under two scenarios: large number of replications and large number of genes. We conduct simulations to evaluate the finite sample performance of the data-driven optimal shrinkage estimators and compare them with some existing methods. We construct F-like statistics using these shrinkage variance estimators and apply them to detect differentially expressed genes in a microarray experiment. We also conduct simulations to evaluate performance of these F-like statistics and compare them with some existing methods. Key words and phrases: F-like statistic, gene expression data, inadmissibility, James-Stein shrinkage estimator, loss function. 1.

Abstract: In this review, we have discussed the class-prediction and discovery methods that are applied to gene expression data, along with the implications of the findings. We attempted to present a unified approach that considers both class-prediction and class-discovery. We devoted a substantial part of this review to an overview of pattern classification/recognition methods and discussed important issues such as preprocessing of gene expression data, curse of dimensionality, feature extraction/selection, and measuring or estimating classifier performance. We discussed and summarized important properties such as generalizability (sensitivity to overtraining), built-in feature selection, ability to report prediction strength, and transparency (ease of understanding of the operation) of different class-predictor design approaches to provide a quick and concise reference. We have also covered the topic of biclustering, which is an emerging clustering method that processes the entries of the gene expression data matrix in both gene and sample directions simultaneously, in detail. 1.

We present several applications of non-linear data modeling, using principal manifolds and principal graphs constructed using the metaphor of elasticity (elastic principal graph approach). These approaches are generalizations of the Kohonen’s self-organizing maps, a class of artificial neural networks. On several examples we show advantages of using non-linear objects for data approximation in comparison to the linear ones. We propose four numerical criteria for comparing linear and non-linear mappings of datasets into the spaces of lower dimension. The examples are taken from comparative political science, from analysis of high-throughput data in molecular biology, from analysis of dynamical systems.

doi:10.1093/bioinformatics/btl190 Independent component analysis-based penalized discriminant method for tumor classification using gene expression data

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PURPOSE. Leber congenital amaurosis (LCA) and juvenile retinitis pigmentosa (RP) cause severe visual impairment early in life. Thus far, mutations in 13 genes have been associated with autosomal recessive LCA and juvenile RP. The purpose of this study was to use homozygosity mapping to identify mutations in known LCA and juvenile RP genes. METHODS. The genomes of 93 consanguineous and nonconsanguineous patients with LCA and juvenile RP were analyzed for homozygous chromosomal regions by using SNP microarrays. This patient cohort was highly selected, as mutations in the known genes had been excluded with the LCA mutation chip, or a significant number of LCA genes had been excluded by comprehensive mutation analysis. Known LCA and juvenile RP genes residing in the identified homozygous regions were analyzed by sequencing. Detailed ophthalmic examinations were performed on the genotyped patients. RESULTS. Ten homozygous mutations, including seven novel mutations, were identified in the CRB1, LRAT, RPE65, and TULP1 genes in 12 patients. Ten patients were from consanguineous marriages, but in two patients no consanguinity was reported. In 10 of the 12 patients, the causative mutation was present in the largest or second largest homozygous segment of the patient's genome. CONCLUSIONS. Homozygosity mapping using SNP microarrays identified mutations in a significant proportion (30%) of consanguineous patients with LCA and juvenile RP and in a small number (3%) of nonconsanguineous patients. Significant homozygous regions which did not map to known LCA or juvenile RP genes and may be instrumental in identifying novel disease genes were detected in 33 patients. (Invest Ophthalmol Vis Sci. 2007;48:5690 -5698)

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Motivation: We describe an approach to normalizing spotted microarray data, based on a physically motivated calibration model. This model consists of two major components, describing the hybridization of target transcripts to their corresponding probes on the one hand, and the measurement of fluorescence from the hybridized, labeled target on the other hand. The model parameters and error distributions are estimated from external control spikes. Results: Using a publicly available data set, we show that our procedure is capable of adequately removing the typical non-linearities of the data, without making any assumptions on the distribution of differences in gene expression from one biological sample to the next. Since our model links target concentration to measured intensity, we show how absolute expression values of target transcripts in the hybridization solution can be estimated up to a certain degree. Contact: