Bayesian biomarker identification based on marker-expression proteomics data

Biomarker Indentification Using Bayesian Variable Selection Based on Marker-expression-proteomics Data

Finding genetic biomarkers and a search of geneticepidemiological factors, can be formulated as a statistical problem of variable selection, where from a large set of candidates a small number of trait-associated predictors are identified. We illustrate this by analyzing the data available for Chronic Fatigue Syndrome (CFS). CFS is a complex disease from several aspects, e.g. difficult to diagn...

Bayesian Modeling Based on Data from the Internet of Things

The Internet of Things is suggested as the upcoming revolution in the Information and communication technology due to its very high capability of making various businesses and industries more productive and efficient. This productivity comes from the emergence of innovation and the introduction of new capabilities for businesses. Different industries have shown varying reactions to IOT, but wha...

Bayesian inference for biomarker discovery in proteomics: an analytic solution

This paper addresses the question of biomarker discovery in proteomics. Given clinical data regarding a list of proteins for a set of individuals, the tackled problem is to extract a short subset of proteins the concentrations of which are an indicator of the biological status (healthy or pathological). In this paper, it is formulated as a specific instance of variable selection. The originalit...

Identification of prohibitin as a potential biomarker for colorectal carcinoma based on proteomics technology.

Differential protein expression was analyzed in carcinoma tissue to determine the correlation between protein levels and the clinical and pathological parameters of patients with colorectal carcinomas (CRC). Two-dimensional electrophoresis (2-DE) revealed 40 protein spots that were differentially expressed at two or greater fold difference in CRC that were identified by matrix-assisted laser de...

Bayesian Inference for Gene Expression and Proteomics