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» Learning Rough Set Classifiers from Gene Expressions and Cli...
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KDD
2002
ACM
147views Data Mining» more  KDD 2002»
15 years 10 months ago
Visualized Classification of Multiple Sample Types
The goal of the knowledge discovery and data mining is to extract the useful knowledge from the given data. Visualization enables us to find structures, features, patterns, and re...
Li Zhang, Aidong Zhang, Murali Ramanathan
83
Voted
BMCBI
2008
128views more  BMCBI 2008»
14 years 9 months ago
Meta-analysis of breast cancer microarray studies in conjunction with conserved cis-elements suggest patterns for coordinate reg
Background: Gene expression measurements from breast cancer (BrCa) tumors are established clinical predictive tools to identify tumor subtypes, identify patients showing poor/good...
David D. Smith, Pål Sætrom, Ola R. Sn&...
MICCAI
2008
Springer
15 years 10 months ago
Cortical Surface Thickness as a Classifier: Boosting for Autism Classification
We study the problem of classifying an autistic group from controls using structural image data alone, a task that requires a clinical interview with a psychologist. Because of the...
Vikas Singh, Lopamudra Mukherjee, Moo K. Chung
CSB
2004
IEEE
135views Bioinformatics» more  CSB 2004»
15 years 1 months ago
Selection of Patient Samples and Genes for Outcome Prediction
Gene expression profiles with clinical outcome data enable monitoring of disease progression and prediction of patient survival at the molecular level. We present a new computatio...
Huiqing Liu, Jinyan Li, Limsoon Wong
BMCBI
2006
129views more  BMCBI 2006»
14 years 9 months ago
Identifying genes that contribute most to good classification in microarrays
Background: The goal of most microarray studies is either the identification of genes that are most differentially expressed or the creation of a good classification rule. The dis...
Stuart G. Baker, Barnett S. Kramer