"BACKGROUND: In this study, differential gene expression analysis using complementary DNA (cDNA) libraries has been improved. Firstly by the introduction of an accurate method of assigning Expressed Sequence Tags (ESTs) to genes and secondly, by using a novel likelihood ratio statistical scoring of differential gene expression between two pools of cDNA libraries. These methods were applied to the latest available cell line and bulk tissue cDNA libraries in a two-step screen to predict novel tumour endothelial markers.

"ABSTRACT: BACKGROUND: Pancreatic cancer is the fourth leading cause of cancer death in the United States. Consequently, identification of clinically relevant biomarkers for the early detection of this cancer type is urgently needed. In recent years, proteomics profiling techniques combined with various data analysis methods have been successfully used to gain critical insights into processes and mechanisms underlying pathologic conditions, particularly as they relate to cancer.

"The number of potential molecular markers is constantly increasing. However, for most malignancies there is no simple test to detect early-stage tumours that is useful for screening purposes because most biomarkers have poor sensitivity or specificity, or other clinical value. One approach to increase their value is to measure several biomarkers at a time. The additional information should always yield a test more able to distinguish between patients and healthy individuals, and ideally between different kinds of tumour.

"In biomarker identification using mass spectrometry, normalization makes it possible to compare mass spectra obtained from different samples. However, the relative influence of different normalization methods is an unexplored topic. In this study, we compared the most widely used normalization methods in a systemic manner to investigate impact of normalization. According to our experimental results, different normalization methods result in the selection of different features of different discriminatory power."

"MOTIVATION: Diseases normally progress in several stages. Therefore, there exist biomarkers corresponding to each stage. To deal with such a multi-category problem including sample stage prediction and biomarker selection, we propose methods for classification and feature selection. The proposed classification method is based on two schemes: error-correcting output coding (ECOC) and pairwise coupling (PWC). The final decision for a test sample prediction is the integration of these two schemes.

"OBJECTIVE: Early detection is paramount to reduce the high death rate of ovarian cancer. Unfortunately, current detection tool is not sensitive. New techniques such as deoxyribonucleic acid (DNA) micro-array and proteomics data are difficult to analyze due to high dimensionality, whereas conventional methods such as blood test are neither sensitive nor specific. METHODS: Thus, a functional model of human pattern recognition known as complementary learning fuzzy neural network (CLFNN) is proposed to aid existing diagnosis methods.

"The concept of covariate adjustment is well established in therapeutic and etiologic studies. However, it has received little attention in the growing area of medical research devoted to the development of markers for disease diagnosis, screening, or prognosis, where classification accuracy, rather than association, is of primary interest.

"It is a consensus in microarray analysis that identifying potential local patterns, characterized by coherent groups of genes and conditions, may shed light on the discovery of previously undetectable biological cellular processes of genes as well as macroscopic phenotypes of related samples. In order to simultaneously cluster genes and conditions, we have previously developed a fast co-clustering algorithm, Minimum Sum-Squared Residue Co-clustering (MSSRCC), which employs an alternating minimization scheme and generates what we call co-clusters in a checkerboard structure.

"We are studying variable selection in multiple regression models in which molecular markers and/or gene-expression measurements as well as intensity measurements from protein spectra serve as predictors for the outcome variable (i.e., trait or disease state). Finding genetic biomarkers and searching genetic-epidemiological factors can be formulated as a statistical problem of variable selection, in which, from a large set of candidates, a small number of trait-associated predictors are identified. We illustrate our approach by analyzing the data available for chronic fatigue syndrome (CFS).

"Gene expression data sets hold the promise to provide cancer diagnosis on the molecular level. However, using all the gene profiles for diagnosis may be suboptimal. Detection of the molecular signatures not only reduces the number of genes needed for discrimination purposes, but may elucidate the roles they play in the biological processes. Therefore, a central part of diagnosis is to detect a small set of tumor biomarkers which can be used for accurate multiclass cancer classification. This task calls for effective multiclass classifiers with built-in biomarker selection mechanism.