Biomedical subjects

Debashis Ghosh

Publications and source records attributed to Debashis Ghosh.

58 records · Page 4Linked to original sources

PubMed2002

Mixture modelling of gene expression data from microarray experiments.

MOTIVATION: Hierarchical clustering is one of the major analytical tools for gene expression data from microarray experiments. A major problem in the interpretation of the output from these procedures is assessing the reliability of the clustering results. We address this issue by developing a mixture model-based approach for the analysis of microarray data. Within this framework, we present novel algorithms for clustering genes and samples. One of the byproducts of our method is a probabilistic measure for the number of true clusters in the data. RESULTS: The proposed methods are illustrated by application to microarray datasets from two cancer studies; one in which malignant melanoma is profiled (Bittner et al., Nature, 406, 536-540, 2000), and the other in which prostate cancer is profiled (Dhanasekaran et al., 2001, submitted).

Algorithms↗

PubMed2002

Singular value decomposition regression models for classification of tumors from microarray experiments.

An important problem in the analysis of microarray data is correlating the high-dimensional measurements with clinical phenotypes. In this paper, we develop predictive models for associating gene expression data from microarray experiments with such outcomes. They are based on the singular value decomposition. We propose new algorithms for performing gene selection and gene clustering based on these predictive models. The estimation procedure using the regression models occurs in two stages. First, the gene expression measurements are transformed using the singular value decomposition. The regression parameters in the model linking the principal components with the clinical responses are then estimated using maximum likelihood. We demonstrate the application of the methodology to data from a breast cancer study.

Breast Neoplasms↗

PubMedDate not supplied

ONCOMINE: a cancer microarray database and integrated data-mining platform.

DNA microarray technology has led to an explosion of oncogenomic analyses, generating a wealth of data and uncovering the complex gene expression patterns of cancer. Unfortunately, due to the lack of a unifying bioinformatic resource, the majority of these data sit stagnant and disjointed following publication, massively underutilized by the cancer research community. Here, we present ONCOMINE, a cancer microarray database and web-based data-mining platform aimed at facilitating discovery from genome-wide expression analyses. To date, ONCOMINE contains 65 gene expression datasets comprising nearly 48 million gene expression measurements form over 4700 microarray experiments. Differential expression analyses comparing most major types of cancer with respective normal tissues as well as a variety of cancer subtypes and clinical-based and pathology-based analyses are available for exploration. Data can be queried and visualized for a selected gene across all analyses or for multiple genes in a selected analysis. Furthermore, gene sets can be limited to clinically important annotations including secreted, kinase, membrane, and known gene-drug target pairs to facilitate the discovery of novel biomarkers and therapeutic targets.

Databases, Genetic↗

PubMedDate not supplied

Risk factors for respiratory symptoms and asthma in the residential environment of 5th grade schoolchildren in Taipei, Taiwan.

This study examined the association of residential environmental factors with respiratory symptoms and asthma in 2,290 fifth graders in Taipei, Taiwan. A self-report survey questionnaire elicited experiences of respiratory symptoms, disease history, and characteristics of residential environmental factors from schoolchildren. The proportion of schoolchildren having physician-diagnosed asthma was 9.8% and suspected asthma was 16.1%. The proportions of having respiratory symptoms in the past 12 months ranged from 9.8% for wheezing without a cold to 40.5% for exercise-induced cough. Higher proportions of boys had non-exercise-induced respiratory symptoms, physician-diagnosed asthma, and suspected asthma than girls (p < 0.05). Exposure to odoriferous chemical vapor was significantly associated with all seven respiratory symptoms considered in the study (p < 0.05). After adjusting for confounding factors including residential districts, gender, diagnosed allergy, and parental history of respiratory symptoms, odoriferous chemical vapors, gas leaks, dehumidifier use, presence of cockroaches at home, and leaky water/water puddle at home were significantly associated with the proportions of physician-diagnosed asthma or suspected asthma of the schoolchildren. The adjusted odds ratio (aOR) of physician-diagnosed asthma was 2.35 (95% confidential interval = 1.45-3.82) for odoriferous chemical vapor. The aOR of suspected asthma measure was 2.14 (95% CI = 1.40-3.26) for odoriferous chemical vapor. Odoriferous chemical vapor was the major risk factor of respiratory illness in the residential environment of schoolchildren in Taipei. Other household risk factors included gas leaks, dampness, and cockroaches at home.

Air Pollution↗