iRefIndex: A consolidated protein interaction database with provenance

Background  

Interaction data for a given protein may be spread across multiple databases. We set out to create a unifying index that would facilitate searching for these data and that would group together redundant interaction data while recording the methods used to perform this grouping.     

A double classification tree search algorithm for index SNP selection

Background  

In population-based studies, it is generally recognized that single nucleotide polymorphism (SNP) markers are not independent. Rather, they are carried by haplotypes, groups of SNPs that tend to be coinherited. It is thus possible to choose a much smaller number of SNPs to use as indices for identifying haplotypes or haplotype blocks in genetic association studies. We refer to these characteristic SNPs as index SNPs. In order to reduce costs and work, a minimum number of index SNPs that can distinguish all SNP and haplotype patterns should be chosen. Unfortunately, this is an NP-complete problem, requiring brute force algorithms that are not feasible for large data sets.     

RCMAT: a regularized covariance matrix approach to testing gene sets

Background  

Gene sets are widely used to interpret genome-scale data. Analysis techniques that make better use of the correlation structure of microarray data while addressing practical "n<p" concerns could provide a real increase in power. However correlation structure is hard to estimate with typical genomics sample sizes. In this paper we present an extension of a classical multivariate procedure that confronts this challenge by the use of a regularized covariance matrix.     

Multivariate analysis of microarray data: differential expression and differential connection

Background  

Typical analysis of microarray data ignores the correlation between gene expression values. In this paper we present a model for microarray data which specifically allows for correlation between genes. As a result we combine gene network ideas with linear models and differential expression.     

On the persistence of supplementary resources in biomedical publications

Background  

Providing for long-term and consistent public access to scientific data is a growing concern in biomedical research. One aspect of this problem can be demonstrated by evaluating the persistence of supplementary data associated with published biomedical papers.     

Improving the analysis of designed studies by combining statistical modelling with study design information

Background  

In the fields of life sciences, so-called designed studies are used for studying complex biological systems. The data derived from these studies comply with a study design aimed at generating relevant information while diminishing unwanted variation (noise). Knowledge about the study design can be used to decompose the total data into data blocks that are associated with specific effects. Subsequent statistical analysis can be improved by this decomposition if these are applied on selected combinations of effects.     

Longitudinal analysis of the vaginal microflora in pregnancy suggests that L. crispatus promotes the stability of the normal vaginal microflora and that L. gasseri and/or L. iners are more conducive to the occurrence of abnormal vaginal microflora

Background  

Despite their antimicrobial potential, vaginal lactobacilli often fail to retain dominance, resulting in overgrowth of the vagina by other bacteria, as observed with bacterial vaginosis. It remains elusive however to what extent interindividual differences in vaginal Lactobacillus community composition determine the stability of this microflora. In a prospective cohort of pregnant women we studied the stability of the normal vaginal microflora (assessed on Gram stain) as a function of the presence of the vaginal Lactobacillus index species (determined through culture and molecular analysis with tRFLP).     

Detection of divergent genes in microbial aCGH experiments

Background  

Array-based comparative genome hybridization (aCGH) is a tool for rapid comparison of genomes from different bacterial strains. The purpose of such analysis is to detect highly divergent or absent genes in a sample strain compared to an index strain. Development of methods for analyzing aCGH data has primarily focused on copy number abberations in cancer research. In microbial aCGH analyses, genes are typically ranked by log-ratios, and classification into divergent or present is done by choosing a cutoff log-ratio, either manually or by statistics calculated from the log-ratio distribution. As experimental settings vary considerably, it is not possible to develop a classical discriminant or statistical learning approach.     

Algorithm for multi-curve-fitting with shared parameters and a possible application in evoked compound action potential measurements

Background  

Experimental results are commonly fitted by determining parameter values of suitable mathematical expressions. In case a relation exists between different data sets, the accuracy of the parameters obtained can be increased by incorporating this relationship in the fitting process instead of fitting the recordings separately.     

Portraits of breast cancer progression

Background  

Clustering analysis of microarray data is often criticized for giving ambiguous results because of sensitivity to data perturbation or clustering techniques used. In this paper, we describe a new method based on principal component analysis and ensemble consensus clustering that avoids these problems.     

Sample phenotype clusters in high-density oligonucleotide microarray data sets are revealed using Isomap,a nonlinear algorithm

Background  

Life processes are determined by the organism's genetic profile and multiple environmental variables. However the interaction between these factors is inherently non-linear [1]. Microarray data is one representation of the nonlinear interactions among genes and genes and environmental factors. Still most microarray studies use linear methods for the interpretation of nonlinear data. In this study, we apply Isomap, a nonlinear method of dimensionality reduction, to analyze three independent large Affymetrix high-density oligonucleotide microarray data sets.     

A database of phylogenetically atypical genes in archaeal and bacterial genomes,identified using the <Emphasis Type="Italic">DarkHorse</Emphasis> algorithm

Background  

The process of horizontal gene transfer (HGT) is believed to be widespread in Bacteria and Archaea, but little comparative data is available addressing its occurrence in complete microbial genomes. Collection of high-quality, automated HGT prediction data based on phylogenetic evidence has previously been impractical for large numbers of genomes at once, due to prohibitive computational demands. DarkHorse, a recently described statistical method for discovering phylogenetically atypical genes on a genome-wide basis, provides a means to solve this problem through lineage probability index (LPI) ranking scores. LPI scores inversely reflect phylogenetic distance between a test amino acid sequence and its closest available database matches. Proteins with low LPI scores are good horizontal gene transfer candidates; those with high scores are not.     

Combinatorial gene therapy renders increased survival in cirrhotic rats

Background  

Liver fibrosis ranks as the second cause of death in México's productive-age population. This pathology is characterized by acummulation of fibrillar proteins in hepatic parenchyma causing synthetic and metabolic disfunction. Remotion of excessive fibrous proteins might result in benefit for subjects increasing survival index. The goal of this work was to find whether the already known therapeutical effect of human urokinase Plasminogen Activator and human Matrix Metalloprotease 8 extends survival index in cirrhotic animals.     

A comparative study of a theoretical neural net model with MEG data from epileptic patients and normal individuals

Objective  

The aim of this study was to compare a theoretical neural net model with MEG data from epileptic patients and normal individuals.     

Use of cerebral state index to predict long-term unconsciousness in patients after elective craniotomy with delay recovery

Background  

The major difficulty in postoperative care in patients after craniotomy is to distinguish the intracranial deficits from the residual effect of general anesthesia. In present study, we used cerebral state index (CSI) monitoring in patients after craniotomy with delayed recovery, and evaluated the prediction probability of CSI for long-term postoperative unconsciousness.     

Deterministic Effects Propagation Networks for reconstructing protein signaling networks from multiple interventions

Background  

Modern gene perturbation techniques, like RNA interference (RNAi), enable us to study effects of targeted interventions in cells efficiently. In combination with mRNA or protein expression data this allows to gain insights into the behavior of complex biological systems.     

Prediction of highly expressed genes in microbes based on chromatin accessibility

Background  

It is well known that gene expression is dependent on chromatin structure in eukaryotes and it is likely that chromatin can play a role in bacterial gene expression as well. Here, we use a nucleosomal position preference measure of anisotropic DNA flexibility to predict highly expressed genes in microbial genomes. We compare these predictions with those based on codon adaptation index (CAI) values, and also with experimental data for 6 different microbial genomes, with a particular interest in experimental data from Escherichia coli. Moreover, position preference is examined further in 328 sequenced microbial genomes.     

Diversification of the cullin family

Background  

Cullins are proteins involved in ubiquitination through their participation in multisubunit ubiquitin ligase complexes. In this study, I use comparative genomic data to establish the pattern of emergence and diversification of cullins in eukaryotes.     

Empirical Bayes models for multiple probe type microarrays at the probe level

Background  

When analyzing microarray data a primary objective is often to find differentially expressed genes. With empirical Bayes and penalized t-tests the sample variances are adjusted towards a global estimate, producing more stable results compared to ordinary t-tests. However, for Affymetrix type data a clear dependency between variability and intensity-level generally exists, even for logged intensities, most clearly for data at the probe level but also for probe-set summarizes such as the MAS5 expression index. As a consequence, adjustment towards a global estimate results in an intensity-level dependent false positive rate.