Biclustering expression data based on expanding localized substructures
Yükleniyor...
Dosyalar
Tarih
2009
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer-Verlag Berlin Heidelberg
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Biclustering gene expression data is the problem of extracting submatrices of genes and conditions exhibiting significant correlation across both the rows and the columns of a data matrix of expression values. We provide a method, LEB (Localize-and-Extract Biclusters) which reduces the search space into local neighborhoods within the matrix by first localizing correlated structures. The localization procedure takes its roots from effective use of graph-theoretical methods applied to problems exhibiting a similar structure to that of biclustering. Once interesting structures are localized the search space reduces to small neighborhoods and the biclusters are extracted from these localities. We evaluate the effectiveness of our method with extensive experiments both using artificial and real datasets.
Açıklama
Anahtar Kelimeler
Adaptive noise , Algorithms, Biclustering, Biclustering algorithm, Biclusters, Bioinformatics, Biology, Bipartite graph, Data matrices, Enrichment ratio, Expression data, Gene, Gene expression, Gene expression data, Localization procedure, Localize substructure, Matrix, Matrix algebra, Microarray data, Real data sets, Search spaces, Sub-matrices, Yeast cell cycle
Kaynak
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
WoS Q Değeri
Q4
Scopus Q Değeri
Q3
Cilt
5462 LNBI
Sayı
Künye
Erten, C. & Sözdinler, M. (2009). Biclustering expression data based on expanding localized substructures. Paper presented at the Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5462 224-235. doi:10.1007/978-3-642-00727-9_22
