NDLI: Comparison of unsupervised feature selection methods for high-dimensional regression problems in prediction of peptide binding affinity

Content Provider	IEEE Xplore Digital Library
Author	Sarac, F. Uslan, V. Seker, H. Bouridane, A.
Copyright Year	2015
Description	Author affiliation: Fac. of Eng., Mevlana Univ., Konya, Turkey (Uslan, V.) \|\| Fac. of Eng. & Environ., Univ. of Northumbria at Newcastle, Newcastle upon Tyne, UK (Sarac, F.; Seker, H.; Bouridane, A.)
Abstract	Identification of robust set of predictive features is one of the most important steps in the construction of clustering, classification and regression models from many thousands of features. Although there have been various attempts to select predictive feature sets from high-dimensional data sets in classification and clustering, there is a limited attempt to study it in regression problems. As semi-supervised and supervised feature selection methods tend to identify noisy features in addition to discriminative variables, unsupervised feature selection methods (USFSMs) are generally regarded as more unbiased approach. Therefore, in this study, along with the entire feature set, four different USFSMs are considered for the quantitative prediction of peptide binding affinities being one of the most challenging post-genome regression problems of very high-dimension comparted to extremely small size of samples. As USFSMs are independent of any predictive method, support vector regression was then utilised to assess the quality of prediction. Given three different peptide binding affinity data sets, the results suggest that the regression performance of USFMs depends generally on the datasets. There is no particular method that yields the best performance compared to their performances in the classification problems. However, a closer investigation of the results appears to suggest that the spectral regression-based approach yields slightly better performance. To the best of our knowledge, this is the first study that presents comprehensive comparison of USFSMs in such high-dimensional regression problems, particularly in biological domain with an application in the prediction of peptide binding affinity, and provides a number of practical suggestions for future practitioners.
Starting Page	8173
Ending Page	8176
File Size	671069
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7320291
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Peptides Predictive models Support vector machines Noise measurement Clustering algorithms Prediction algorithms
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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