Open AccessReview on Analysis of Gene Expression Data Using Biclustering Approaches
Author(s) -
S. Anitha,
Dr.C.P. Chandran
Publication year - 2016
Publication title -
bonfring international journal of data mining
Language(s) - English
Resource type - Journals
eISSN - 2277-5048
pISSN - 2250-107X
DOI - 10.9756/bijdm.8135
Subject(s) - biclustering , block matrix , gene , set (abstract data type) , expression (computer science) , data set , computational biology , homogeneous , computer science , data matrix , matrix (chemical analysis) , gene expression , table (database) , data mining , mathematics , genetics , biology , combinatorics , artificial intelligence , cluster analysis , clade , physics , chemistry , phylogenetics , cure data clustering algorithm , programming language , correlation clustering , chromatography , quantum mechanics , eigenvalues and eigenvectors
In this paper, survey on biclustering approaches for Gene Expression Data (GED) is carried out. Some of the issues are Correlation, Class discovery, Coherent biclusters and coregulated biclusters. Each table entry is called an expression value and reflects the behaviour of the gene in a row in the situation in column. The goal of biclustering is to identify "homogeneous" submatrices. Given a gene expression data matrix D=G×C= {dij} (here i € (1, n), j € (1, m)) is a real-valued n×m matrix, here G is a set of n genes {g1, g2…, gn}, C a set of m biological conditions {c1, c2…, cn}.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom