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» Class discovery in gene expression data
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RECOMB
2002
Springer
14 years 7 months ago
A new approach to analyzing gene expression time series data
We present algorithms for time-series gene expression analysis that permit the principled estimation of unobserved timepoints, clustering, and dataset alignment. Each expression p...
Ziv Bar-Joseph, Georg Gerber, David K. Gifford, To...
BMCBI
2007
207views more  BMCBI 2007»
13 years 7 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...
CSB
2003
IEEE
130views Bioinformatics» more  CSB 2003»
14 years 26 days ago
Latent Structure Models for the Analysis of Gene Expression Data
Cluster methods have been successfully applied in gene expression data analysis to address tumor classification. By grouping tissue samples into homogeneous subsets, more systema...
Dong Hua, Dechang Chen, Xiuzhen Cheng, Abdou Youss...
ECAI
2004
Springer
13 years 11 months ago
Avoiding Data Overfitting in Scientific Discovery: Experiments in Functional Genomics
Functional genomics is a typical scientific discovery domain characterized by a very large number of attributes (genes) relative to the number of examples (observations). The dang...
Dragan Gamberger, Nada Lavrac
BMCBI
2005
163views more  BMCBI 2005»
13 years 7 months ago
Rank-invariant resampling based estimation of false discovery rate for analysis of small sample microarray data
Background: The evaluation of statistical significance has become a critical process in identifying differentially expressed genes in microarray studies. Classical p-value adjustm...
Nitin Jain, HyungJun Cho, Michael O'Connell, Jae K...