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Ortega-Martorell, S, Olier, I, Julia-Sape, M and Arus, C (2010) SpectraClassifier 1.0: a user friendly, automated MRS-based classifier-development system. BMC Bioinformatics, 11. ISSN 1471-2105
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SpectraClassifier 1.0: a user friendly, automated MRS-based classifier-development system.pdf - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview |
Abstract
Background: SpectraClassifier (SC) is a Java solution for designing and implementing Magnetic Resonance Spectroscopy (MRS)-based classifiers. The main goal of SC is to allow users with minimum background knowledge of multivariate statistics to perform a fully automated pattern recognition analysis. SC incorporates feature selection (greedy stepwise approach, either forward or backward), and feature extraction (PCA). Fisher Linear Discriminant Analysis is the method of choice for classification. Classifier evaluation is performed through various methods: display of the confusion matrix of the training and testing datasets; K-fold cross-validation, leave-one-out and bootstrapping as well as Receiver Operating Characteristic (ROC) curves.
Results: SC is composed of the following modules: Classifier design, Data exploration, Data visualisation, Classifier evaluation, Reports, and Classifier history. It is able to read low resolution in-vivo MRS (single-voxel and multi-voxel) and high resolution tissue MRS (HRMAS), processed with existing tools (jMRUI, INTERPRET, 3DiCSI or TopSpin). In addition, to facilitate exchanging data between applications, a standard format capable of storing all the information needed for a dataset was developed. Each functionality of SC has been specifically validated with real data with the purpose of bug-testing and methods validation. Data from the INTERPRET project was used.
Conclusions: SC is a user-friendly software designed to fulfil the needs of potential users in the MRS community. It accepts all kinds of pre-processed MRS data types and classifies them semi-automatically, allowing spectroscopists to concentrate on interpretation of results with the use of its visualisation tools.
| Item Type: | Article |
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| Uncontrolled Keywords: | 06 Biological Sciences, 08 Information And Computing Sciences, 01 Mathematical Sciences |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Computer software R Medicine > RM Therapeutics. Pharmacology |
| Divisions: | Applied Mathematics (merged with Comp Sci 10 Aug 20) |
| Publisher: | BioMed Central |
| Related URLs: | |
| Date Deposited: | 23 May 2018 09:13 |
| Last Modified: | 04 Sep 2021 10:28 |
| DOI or ID number: | 10.1186/1471-2105-11-106 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/8701 |
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