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Gene selection for cancer classification with the help of bees
JM Moosa
,
R Shakur
, M Kaykobad
, MS Rahman
School of Applied Sciences
Research output
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Contribution to journal
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Article
›
peer-review
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Keyphrases
Gene number
100%
Gene Selection
100%
Cancer Classification
100%
Gene Expression Data
66%
Modified Artificial Bee Colony Algorithm
66%
Artificial Bee Colony Algorithm
66%
Major Components
33%
Clinical Oncology
33%
Prediction Accuracy
33%
Accurate Classification
33%
Minimum number
33%
Microarray Data
33%
Bioinformatics
33%
Clinical Genetics
33%
Cutout
33%
Publicly Available Dataset
33%
Ant Colony Optimization
33%
Classification Results
33%
Medical Diagnosis
33%
Search Equation
33%
Biologically Relevant
33%
Pheromone
33%
Mathematics
Total Number
100%
Communicates
100%
Medical Diagnosis
100%
Predictive Accuracy
100%
Biochemistry, Genetics and Molecular Biology
Genetics
100%
Gene Expression Data
100%
Cancer Classification
100%
Pheromone
50%
Bioinformatics
50%
Computer Science
Artificial Bee Colony Algorithm
100%
Gene Expression Data
50%
Major Component
25%
Optimization Algorithm
25%
Predictive Accuracy
25%
Ant Colony Optimization
25%
Medical Diagnosis
25%
Microarray Data
25%
classification result
25%
Bioinformatics
25%
Neuroscience
Gene Expression
100%
Pheromone
50%
Engineering
Bioinformatics
100%