NDLI: Feature selections using AdaBoost: Application in gene-gene interaction detection

Content Provider	IEEE Xplore Digital Library
Author	Assareh, A. Volkert, L.G. Jing Li
Copyright Year	2012
Description	Author affiliation: CS Department, Kent State University, Kent, Ohio, USA (Assareh, A.; Volkert, L.G.) \|\| EECS Department, Case Western Reserve University, Cleveland, Ohio, USA (Jing Li)
Abstract	One of the main goals of genome wide association studies (GWAS) has been detecting the gene-gene interactions, also known as epistasis in a broad sense, underlying complex diseases. The ability of decision trees and their ensembles to capture interactions among input variable has attracted attention among computational biologists for this aim. However, individual decision trees suffer from some limitations including data fragmentation and representational problem that can impact the epistasis detection performance of their ensembles when not taken into account. Here we take a closer look at feature selection capability of AdaBoost in the realm of epistasis detection and the effect of tuning the weak classifiers on its performance. We also explore the efficacy of applying different statistical and information theoretic strategies in tandem with AdaBoost in order to improve its performance. The results show that the performance of AdaBoost is more sensitive to the parameters settings of the weak learner when risk allele frequencies are low, which can be explained with respect to the data fragmentation phenomenon. Also depending on the model of interaction between the risk SNPs different criterion might excel in the second stage.
Starting Page	831
Ending Page	837
File Size	1815952
Page Count	7
File Format	PDF
ISBN	9781467327466
e-ISBN	9781467327473
e-ISBN	9781467327442
DOI	10.1109/BIBMW.2012.6470248
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Additives AdaBoost GWAS epistasis decision trees Data models Decision trees Bioinformatics Mutual information Diseases Logistics
Content Type	Text
Resource Type	Article

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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
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