Biomedical subjects

Victor Trevino

Publications and source records attributed to Victor Trevino.

4 recordsLinked to original sources

PubMed2006

GALGO: an R package for multivariate variable selection using genetic algorithms.

SUMMARY: The development of statistical models linking the molecular state of a cell to its physiology is one of the most important tasks in the analysis of Functional Genomics data. Because of the large number of variables measured a comprehensive evaluation of variable subsets cannot be performed with available computational resources. It follows that an efficient variable selection strategy is required. However, although software packages for performing univariate variable selection are available, a comprehensive software environment to develop and evaluate multivariate statistical models using a multivariate variable selection strategy is still needed. In order to address this issue, we developed GALGO, an R package based on a genetic algorithm variable selection strategy, primarily designed to develop statistical models from large-scale datasets.

Algorithms↗

PubMed2006

Synovial fluid leukocyte apoptosis is inhibited in patients with very early rheumatoid arthritis.

Synovial leukocyte apoptosis is inhibited in established rheumatoid arthritis (RA). In contrast, high levels of leukocyte apoptosis are seen in self-limiting crystal arthritis. The phase in the development of RA at which the inhibition of leukocyte apoptosis is first apparent, and the relationship between leukocyte apoptosis in early RA and other early arthritides, has not been defined. We measured synovial fluid leukocyte apoptosis in very early arthritis and related this to clinical outcome. Synovial fluid was obtained at presentation from 81 patients with synovitis of < or = 3 months duration. The percentages of apoptotic neutrophils and lymphocytes were assessed on cytospin preparations. Patients were assigned to diagnostic groups after 18 months follow-up. The relationship between leukocyte apoptosis and patient outcome was assessed. Patients with early RA had significantly lower levels of neutrophil apoptosis than patients who developed non-RA persistent arthritis and those with a resolving disease course. Similarly, lymphocyte apoptosis was absent in patients with early RA whereas it was seen in patients with other early arthritides. The inhibition of synovial fluid leukocyte apoptosis in the earliest clinically apparent phase of RA distinguishes this from other early arthritides. The mechanisms for this inhibition may relate to the high levels of anti-apoptotic cytokines found in the early rheumatoid joint (e.g. IL-2, IL-4, IL-15 GMCSF, GCSF). It is likely that this process contributes to an accumulation of leukocytes in the early rheumatoid lesion and is involved in the development of the microenvironment required for persistent RA.

Adult↗

PubMed2005

Analysis of host response to bacterial infection using error model based gene expression microarray experiments.

A key step in the analysis of microarray data is the selection of genes that are differentially expressed. Ideally, such experiments should be properly replicated in order to infer both technical and biological variability, and the data should be subjected to rigorous hypothesis tests to identify the differentially expressed genes. However, in microarray experiments involving the analysis of very large numbers of biological samples, replication is not always practical. Therefore, there is a need for a method to select differentially expressed genes in a rational way from insufficiently replicated data. In this paper, we describe a simple method that uses bootstrapping to generate an error model from a replicated pilot study that can be used to identify differentially expressed genes in subsequent large-scale studies on the same platform, but in which there may be no replicated arrays. The method builds a stratified error model that includes array-to-array variability, feature-to-feature variability and the dependence of error on signal intensity. We apply this model to the characterization of the host response in a model of bacterial infection of human intestinal epithelial cells. We demonstrate the effectiveness of error model based microarray experiments and propose this as a general strategy for a microarray-based screening of large collections of biological samples.

Bacterial Infections↗

PubMed2004

Making sense of molecular signatures in the immune system.

The development of Functional Genomics technologies has opened new avenues to investigate the complexity of the immune system. Microarray technology has been particularly successful because of its relatively low cost and high genome coverage. Consequently to our ability to monitor the expression of a significant proportion of an organism genome, our understanding of the molecular dynamics behind cell differentiation and cell response has greatly improved. Molecular signatures associated to immune cells have provided important tools to investigate the molecular basis of diseases and have been often associated to diagnostic and prognostic markers. The availability of such large collection of data has stimulated the application of complex machine learning techniques in the attempt to link molecular signatures and cell physiology. Here we review the most recent developments in the analysis of molecular signatures in the immune system.

Animals↗