Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data
Open Access
- 22 March 2005
- journal article
- research article
- Published by Oxford University Press (OUP) in Bioinformatics
- Vol. 21 (10) , 2200-2209
- https://doi.org/10.1093/bioinformatics/bti370
Abstract
Motivation: High-throughput and high-resolution mass spectrometry instruments are increasingly used for disease classification and therapeutic guidance. However, the analysis of immense amount of data poses considerable challenges. We have therefore developed a novel method for dimensionality reduction and tested on a published ovarian high-resolution SELDI-TOF dataset.Keywords
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