Pattern Recognition for Normalized LC-MS Data
Posted 12 Jan, 2009 in Tools
Available Versions
- 2.0 (published)
- 1.0 (Unpublished)
Supporting Documents
- Pipeline Open Proteomics June 2010 (PDF, 712.39 Kb)
This tool is closed source.
| Version | 2.0 - published on 05 May. 2010 |
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| Contributor(s) | Ann Christine Catlin Rosen Center for Advanced Computing George Howlett Purdue University |
| At a glance | This tool provides principal component analysis (PCA), linear discriminate analysis (LDA), and canonical discriminate analysis (CDA) for data clustering on aligned, normalized LC-MS datasets. |
| Screenshots | |
| Description | Pattern recognition approaches fall into two main categories: supervised and unsupervised. Supervised systems require knowledge or data in which the outcome or classification is known ahead of time, so that the system can be trained to recognize and distinguish outcomes. Unsupervised systems cluster or group records without previous knowledge of outcome
or classification. The most frequently used unsupervised pattern recognition approach is principal component analysis (PCA). Other unsupervised methods include hierarchical clustering, k-means, and self organizing maps (SOM).
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| credits | The pattern recognition software was developed by Xiang Zhang, Jiri Adamec, et al. in 2005. The Purdue Discovery Pipeline was created by the Bindley Biosciences Center under the direction of Charles Buck. |
| Cite this work | If you reference this work in a publication, please cite as follows: |
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