Arşiv ve Dokümantasyon Merkezi
Dijital Arşivi

Machine learning algorithms in classification and diagnostic prediction of cancers using gene expression profiling

Basit öğe kaydını göster

dc.contributor Graduate Program in Physics.
dc.contributor.advisor Kurnaz, M. Levent.
dc.contributor.author Altındal, Tuba.
dc.date.accessioned 2023-03-16T10:37:55Z
dc.date.available 2023-03-16T10:37:55Z
dc.date.issued 2006.
dc.identifier.other PHYS 2006 A48
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/13673
dc.description.abstract The performance of certain machine learning algorithms in classification and diagnostic prediction of small round blue cell tumors (SRBCTs) of childhood is investigated. Before classifying samples, including both tumor biopsy material and cell lines, based on their gene expression profiles, dimensionality of the problem is reduced. Dimensionality reduction is achieved in a two-step procedure that includes correlation- based feature selection (CFS) followed by principal components analysis (PCA). To classify the samples into four distinct diagnostic categories, logistic model trees (LMT) and multilayer perceptrons (MLP) are trained. The posterior probabilities provided by LMT and MLP algorithms for each sample are then used to construct a measure, by means of which one might decide whether to classify a sample into one of the diagnostic categories or to reject classifying.
dc.format.extent 30cm.
dc.publisher Thesis (M.S.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2006.
dc.relation Includes appendices.
dc.relation Includes appendices.
dc.subject.lcsh Machine learning.
dc.subject.lcsh Gene expression.
dc.subject.lcsh Cancer -- Diagnosis.
dc.title Machine learning algorithms in classification and diagnostic prediction of cancers using gene expression profiling
dc.format.pages xii, 52 leaves;


Bu öğenin dosyaları

Bu öğe aşağıdaki koleksiyon(lar)da görünmektedir.

Basit öğe kaydını göster

Dijital Arşivde Ara


Göz at

Hesabım