Abstract
Mass spectrometry (MS) is a major tool in proteomics that is evolving at a rapid pace. Significant advances in instrumentation lead to a high-throughput resource field lacking of suitable data driven analysis tools. Major goals in this area involve the detection of reliable biomarkers and their quantitation. To tackle these challenges we propose a novel unsupervised approach utilizing the FABIA biclustering algorithm. The core application is to use the algorithm on MS level 1 data that is preclustered by retention time in order to find similar spectra over all samples. FABIA looks for samples as well as retention times that show similar patterns of m/z ratios. On the one hand the obtained biclusters facilitate the alignment of retention times and on the other hand they help to detect informative biomarkers. In a next step the results can further be utilized for protein quantitation.We show that our approach outperforms competing methods on benchmark data sets and therefore conclude that pivotal contributions to the detection of differentially expression proteins and their quantitation could be made.
| Original language | English |
|---|---|
| Title of host publication | ISMB 2014 Proceedings |
| Number of pages | 1 |
| Publication status | Published - 2014 |
Fields of science
- 303 Health Sciences
- 304 Medical Biotechnology
- 304003 Genetic engineering
- 305 Other Human Medicine, Health Sciences
- 101004 Biomathematics
- 101018 Statistics
- 102 Computer Sciences
- 102001 Artificial intelligence
- 102004 Bioinformatics
- 102010 Database systems
- 102015 Information systems
- 102019 Machine learning
- 106023 Molecular biology
- 106002 Biochemistry
- 106005 Bioinformatics
- 106007 Biostatistics
- 106041 Structural biology
- 301 Medical-Theoretical Sciences, Pharmacy
- 302 Clinical Medicine
JKU Focus areas
- Computation in Informatics and Mathematics
- Nano-, Bio- and Polymer-Systems: From Structure to Function