Sciweavers

877 search results - page 40 / 176
» A Multi-metric Similarity Based Analysis of Microarray Data
Sort
View
97
Voted
BMCBI
2005
124views more  BMCBI 2005»
14 years 11 months ago
ErmineJ: Tool for functional analysis of gene expression data sets
Background: It is common for the results of a microarray study to be analyzed in the context of biologically-motivated groups of genes such as pathways or Gene Ontology categories...
Homin K. Lee, William Braynen, Kiran Keshav, Paul ...
BMCBI
2011
14 years 6 months ago
AnyExpress: Integrated toolkit for analysis of cross-platform gene expression data using a fast interval matching algorithm
Background: Cross-platform analysis of gene express data requires multiple, intricate processes at different layers with various platforms. However, existing tools handle only a s...
Jihoon Kim, Kiltesh Patel, Hyunchul Jung, Winston ...
DATAMINE
2006
176views more  DATAMINE 2006»
14 years 11 months ago
A Bit Level Representation for Time Series Data Mining with Shape Based Similarity
Clipping is the process of transforming a real valued series into a sequence of bits representing whether each data is above or below the average. In this paper, we argue that clip...
Anthony J. Bagnall, Chotirat (Ann) Ratanamahatana,...
SDM
2008
SIAM
157views Data Mining» more  SDM 2008»
15 years 1 months ago
ROC-tree: A Novel Decision Tree Induction Algorithm Based on Receiver Operating Characteristics to Classify Gene Expression Data
Gene expression information from microarray experiments is a primary form of data for biological analysis and can offer insights into disease processes and cellular behaviour. Suc...
M. Maruf Hossain, Md. Rafiul Hassan, James Bailey
BMCBI
2006
119views more  BMCBI 2006»
14 years 12 months ago
LS-NMF: A modified non-negative matrix factorization algorithm utilizing uncertainty estimates
Background: Non-negative matrix factorisation (NMF), a machine learning algorithm, has been applied to the analysis of microarray data. A key feature of NMF is the ability to iden...
Guoli Wang, Andrew V. Kossenkov, Michael F. Ochs