New statistical algorithms for clinical proteomics

Computational Proteomics

计算机科学;物理学

T. Conrade

Others  :  http://drops.dagstuhl.de/opus/volltexte/2006/542/pdf/05471.ConradTim1.ExtAbstract.542.pdf
PID  :  6672

学科分类：计算机科学（综合）

来源: CEUR

【 摘 要 】

Background: Mass spectrometry based screening methods have been recently introduced into clinical proteomics. This boosts the development of a new approach for early disease detection: proteomic pattern analysis. Aim: Find, analyze and compare proteomic patterns in groups of patients having different properties such as disease status or epidemiological parameters (e.g. sex, age) with a new pipeline to enhance sensitivity and specificity. Problems: Mass data acquired from high-throughput platforms frequently are blurred and noisy. This extremely complicates the reliable identification of peaks in general and very small peaks below noise-level in particular. Approach: Apply sophisticated signal preprocessing steps followed by statistical analyzes to purge the raw data and enable the detection of real signals while maintaining information for tracebacks. Results: A new analysis pipeline has been developed capable of finding and analyzing peak patterns discriminating different groups of patients (e.g. male/female, cancer/healthy). First steps towards distributed computing approaches have been incorporated in the design.