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Optimization Based Tumor Classification From Microarray Gene Expression Data

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Optimization Based Tumor Classification from Microarray Gene Expression Data.pdf (289.2Kb)
Author
Dağlıyan, Onur
Üney-Yüksektepe, Fadime
Kavaklı, Halil
Türkay, Metin
Type
Article
Date
2011-02-04
Language
en_US
Metadata
Show full item record
Abstract
Background: An important use of data obtained from microarray measurements is the classification of tumor types with respect to genes that are either up or down regulated in specific cancer types. A number of algorithms have been proposed to obtain such classifications. These algorithms usually require parameter optimization to obtain accurate results depending on the type of data. Additionally, it is highly critical to find an optimal set of markers among those up or down regulated genes that can be clinically utilized to build assays for the diagnosis or to follow progression of specific cancer types. In this paper, we employ a mixed integer programming based classification algorithm named hyper-box enclosure method (HBE) for the classification of some cancer types with a minimal set of predictor genes. This optimization based method which is a user friendly and efficient classifier may allow the clinicians to diagnose and follow progression of certain cancer types. Methodology/Principal Findings: We apply HBE algorithm to some well known data sets such as leukemia, prostate cancer, diffuse large B-cell lymphoma (DLBCL), small round blue cell tumors (SRBCT) to find some predictor genes that can be utilized for diagnosis and prognosis in a robust manner with a high accuracy. Our approach does not require any modification or parameter optimization for each data set. Additionally, information gain attribute evaluator, relief attribute evaluator and correlation-based feature selection methods are employed for the gene selection. The results are compared with those from other studies and biological roles of selected genes in corresponding cancer type are described. Conclusions/Significance: The performance of our algorithm overall was better than the other algorithms reported in the literature and classifiers found in WEKA data-mining package. Since it does not require a parameter optimization and it performs consistently very high prediction rate on different type of data sets, HBE method is an effective and consistent tool for cancer type prediction with a small number of gene markers.
Subject
Bayesian Variable Selection
Partial Least-squares
B-cell lymphomas
Prostate-Cancer
Logistic-Regression
Prediction
Leukemia
Binding
Identification
Organization
Bayesci Değişken Seçimi
Kısmi En Küçük Kareler
B-hücreli Lenfomalar
Prostat Kanseri
Lojistik Regresyon
Tahmin
Lösemi
Bağlayıcı
Kimlik
Organizasyon
URI
http://hdl.handle.net/11413/1441
Collections
  • Makaleler / Articles [70]
  • Scopus Publications [724]
  • WoS Publications [1016]

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İstanbul Kültür University

Hakkında |Politika | Kütüphane | İletişim | Send Feedback | Admin

Istanbul Kültür University, Ataköy Campus E5 Karayolu Üzeri Bakırköy 34158, İstanbul / TURKEY
Copyright © İstanbul Kültür University

Creative Commons Lisansı
IKU Institutional Repository, Creative Commons Alıntı-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.

Designed by  UNIREPOS