NDLI: Influence of algorithmic parameters on marker selection in genomic datasets

Content Provider	IEEE Xplore Digital Library
Author	Vigdideli, T. Bei, E.S. Zervakis, M. Kafetzopoulos, D.
Copyright Year	2012
Description	Author affiliation: Institute of Molecular Biology and Biotechnology, Foundation of Research and Technology Hellas (FORTH), Heraklion Crete, Greece (Kafetzopoulos, D.) \|\| Department of Electronic and Computer Engineering, Technical University of Crete, Chania Crete, Greece (Vigdideli, T.; Bei, E.S.; Zervakis, M.)
Abstract	The biological processes are widely studied by genome analysis leading to a large number of genes, thus making necessary the use of automated evaluation methods. In this study, we examine the influence of algorithmic parameters in the prediction power of a gene signature and in the selection process of the signature itself. We focus on one gene selection approach applied on a dataset of the budding yeast Saccharomyces cerevisiae, using quite different parameters and evaluate the influence on the selected signature. In particular, we adopt a recursive feature elimination process where at each step the prognostic power of the set of remaining genes is evaluated by five different classifiers, as well as by four classifier-fusions schemes. More specifically, we consider the logistic-sigmoid, kernel nearest centroid, kernel minimum squared error, kernel subspace, and support vector machines as classifiers with different parameters and/or kernel functions. We also study four fusion methods in order to reduce uncertainties related to the classifier evaluating the prognostic significance of genes. In all cases, the selection process is embedded into a cross validation scheme in order to enhance the confidence on the generalization of results. We consider the differences of signatures based on gene overlap and also the biological annotation of selected genes, using the MIPS FunCat architecture. We found out that a robust identification of a number of highly differential genes can offer “good” predictive power to the models. Furthermore, the classification accuracy achieved by mixtures of experts can be significantly better than the one of the individual classifiers. We also pointed out that different selection schemes result in a diverse size of gene signature, with differences in the selected genes. Nevertheless, when we annotate the genes of each signature we find that the same biological processes are invoked, with possibly small differences in the relative frequency of participation.
Starting Page	523
Ending Page	528
File Size	712749
Page Count	6
File Format	PDF
ISBN	9781467343572
e-ISBN	9781467343589
DOI	10.1109/BIBE.2012.6399768
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-11
Publisher Place	Cyprus
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Accuracy Training Biology Prediction algorithms Classification algorithms Bioinformatics biological processes classification mixture of experts gene selection Saccharomyces cerevisiae
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in