A high performance profile-biomarker diagnosis for mass spectral profiles.

ABSTRACT:

Background

Although mass spectrometry based proteomics demonstrates an exciting promise in complex diseases diagnosis, it remains an important research field rather than an applicable clinical routine for its diagnostic accuracy and data reproducibility. Relatively less investigation has been done yet in attaining high-performance proteomic pattern classification compared with the amount of endeavours in enhancing data reproducibility.

Methods

In this study, we present a novel machine learning approach to achieve a clinical level disease diagnosis for mass spectral data. We propose multi-resolution independent component analysis, a novel feature selection algorithm to tackle the large dimensionality of mass spectra, by following our local and global feature selection framewo

SUBMITTER: Han H 

PROVIDER: S-EPMC3287485 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature