NDLI: Predictive modeling of therapy response in multiple sclerosis using gene expression data

Content Provider	IEEE Xplore Digital Library
Author	Mostafavi, S. Baranzini, S. Oksernberg, J. Mousavi, P.
Copyright Year	2006
Description	Author affiliation: Sch. of Comput., Queen's Univ., Kingston, Ont. (Mostafavi, S.)
Abstract	Transcription profiling studies reveal important insights in regards to molecular events that manifest in phenotypic outcomes such as response to drug therapy. Construction of computational models that accurately predict therapy response is only possible when precise data measurements, robust feature/gene selection, and advanced computational modeling methods are combined with stringent statistical validation and large scale verification of results. Due to the large number of gene expression measurements in transcriptional profiling studies, feature selection represents a bottleneck when constructing computational models. The degree of compromise between selection of the optimal feature set and computational efficiency results in many choices for candidate gene sets which leads to a wide range of classification accuracies. Furthermore, constructing a classification model using a larger-than-necessary gene set along with small number of samples may cause over-fitting the data, resulting in highly optimistic classification accuracies. In this study we present OSeMA, a fast, robust and accurate gene selection-classification framework which results in construction of classification models that are highly predictive of the rIFNB therapy response in multiple sclerosis patients. We assess the performance of OSeMA on held out test data. Additionally, we extensively evaluate OSeMA by comparing it to an exhaustive combinatorial gene selection-classification approach
Sponsorship	IEEE EMB
Starting Page	5519
Ending Page	5522
File Size	174630
Page Count	4
File Format	PDF
ISBN	1424400325
ISSN	1557170X
DOI	10.1109/IEMBS.2006.259681
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Medical treatment Multiple sclerosis Gene expression Computational modeling Robustness Drugs Large-scale systems Computational efficiency Testing
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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