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» Clustering gene expression patterns
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BMCBI
2007
207views more  BMCBI 2007»
14 years 10 months ago
Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clus
Background: The four heterogeneous childhood cancers, neuroblastoma, non-Hodgkin lymphoma, rhabdomyosarcoma, and Ewing sarcoma present a similar histology of small round blue cell...
Nikhil R. Pal, Kripamoy Aguan, Animesh Sharma, Shu...
BMCBI
2008
104views more  BMCBI 2008»
14 years 10 months ago
Missing value imputation improves clustering and interpretation of gene expression microarray data
Background: Missing values frequently pose problems in gene expression microarray experiments as they can hinder downstream analysis of the datasets. While several missing value i...
Johannes Tuikkala, Laura Elo, Olli Nevalainen, Ter...
IDEAL
2004
Springer
15 years 3 months ago
Visualisation of Distributions and Clusters Using ViSOMs on Gene Expression Data
Microarray datasets are often too large to visualise due to the high dimensionality. The self-organising map has been found useful to analyse massive complex datasets. It can be us...
Swapna Sarvesvaran, Hujun Yin
BIBE
2007
IEEE
176views Bioinformatics» more  BIBE 2007»
15 years 4 months ago
HICCUP: Hierarchical Clustering Based Value Imputation using Heterogeneous Gene Expression Microarray Datasets
Abstract—A novel microarray value imputation method, HICCUP1 , is presented. HICCUP improves upon existing value imputation methods in the several ways. (1) By judiciously integr...
Qiankun Zhao, Prasenjit Mitra, Dongwon Lee, Jaewoo...
IJON
2008
128views more  IJON 2008»
14 years 9 months ago
Independent arrays or independent time courses for gene expression time series data analysis
In this paper we apply three different independent component analysis (ICA) methods, including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA), to gene exp...
Sookjeong Kim, Jong Kyoung Kim, Seungjin Choi