NDLI: Semisupervised Learning for Molecular Profiling

Content Provider	ACM Digital Library
Author	Serafini, Maria Merler, Stefano Furlanello, Cesare Jurman, Giuseppe
Abstract	Class prediction and feature selection are two learning tasks that are strictly paired in the search of molecular profiles from microarray data. Researchers have become aware how easy it is to incur a selection bias effect, and complex validation setups are required to avoid overly optimistic estimates of the predictive accuracy of the models and incorrect gene selections. This paper describes a semisupervised pattern discovery approach that uses the by-products of complete validation studies on experimental setups for gene profiling. In particular, we introduce the study of the patterns of single sample responses (sample-tracking profiles) to the gene selection process induced by typical supervised learning tasks in microarray studies. We originate sample-tracking profiles as the aggregated off-training evaluation of SVM models of increasing gene panel sizes. Genes are ranked by E-RFE, an entropy-based variant of the recursive feature elimination for support vector machines (RFE-SVM). A Dynamic Time Warping (DTW) algorithm is then applied to define a metric between sample-tracking profiles. An unsupervised clustering based on the DTW metric allows automating the discovery of outliers and of subtypes of different molecular profiles. Applications are described on synthetic data and in two gene expression studies.
Starting Page	110
Ending Page	118
Page Count	9
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2005.28
Volume Number	2
Issue Number	2
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-04-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Machine learning, data mining, classifier design and evaluation, feature evaluation and selection, pattern analysis, clustering, similarity measures, biology and genetics, bioinformatics databases.
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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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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