Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency.

Background: Prostate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear.

Analysis of adverse drug reactions using drug and drug target interactions and graph-based methods.

Objective: The purpose of this study was to integrate knowledge about drugs, drug targets, and topological methods. The goals were to build a system facilitating the study of adverse drug events, to make it easier to find possible explanations, and to group similar drug-drug interaction cases in the adverse drug reaction reports from the US Food and Drug Administration (FDA).

Methods: We developed a system that analyses adverse drug reaction (ADR) cases reported by the FDA.