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» Smoothing Gene Expression Using Biological Networks
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BMCBI
2008
167views more  BMCBI 2008»
13 years 7 months ago
Expression profiles of switch-like genes accurately classify tissue and infectious disease phenotypes in model-based classificat
Background: Large-scale compilation of gene expression microarray datasets across diverse biological phenotypes provided a means of gathering a priori knowledge in the form of ide...
Michael Gormley, Aydin Tozeren
IJON
2008
128views more  IJON 2008»
13 years 7 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
JIB
2006
102views more  JIB 2006»
13 years 7 months ago
Analysing Microarray Data using the Multi-functional Immune Ontologiser
Gene expression microarrays are a prominent experimental tool in functional genomics allowing researchers to gain a deeper understanding of biological processes. To date, no such ...
Sabah Khalid, Karl Fraser, Mohsin Khan, Ping Wang,...
BIOCOMP
2009
13 years 5 months ago
Gene Expression Analysis Using Communications, Coding and Information Theory Based Models
- Identification and annotation of all the functional elements in the genome, including genes and regulatory sequences, is a fundamental challenge in genomics and computational bio...
Mohammad Al Bataineh, Lun Huang, Ismaeel Muhamed, ...
CIBCB
2006
IEEE
14 years 1 months ago
A Model-Free Greedy Gene Selection for Microarray Sample Class Prediction
— Microarray data analysis is notoriously challenging as it involves a huge number of genes compared to only a limited number of samples. Gene selection, to detect the most signi...
Yi Shi, Zhipeng Cai, Lizhe Xu, Wei Ren, Randy Goeb...