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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
:
Contribution to journal
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Article
›
peer-review
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Keyphrases
Accurate Classification
33%
Ant Colony Algorithm
33%
Artificial Bee Colony Algorithm
66%
Bioinformatics
33%
Biologically Relevant
33%
Cancer Classification
100%
Classification Results
33%
Clinical Genetics
33%
Clinical Oncology
33%
Cutout
33%
Gene Expression Data
66%
Gene number
100%
Gene Selection
100%
Genetic Nature
33%
Major Components
33%
Medical Diagnosis
33%
Microarray Data
33%
Minimum number
33%
Modified Artificial Bee Colony Algorithm
66%
Pheromone
33%
Prediction Accuracy
33%
Publicly Available Datasets
33%
Search Equation
33%
Computer Science
Ant Colony Optimization
25%
Artificial Bee Colony Algorithm
100%
Bioinformatics
25%
classification result
25%
Gene Expression Data
50%
Optimization Algorithm
25%
Predictive Accuracy
25%
Mathematics
Predictive Accuracy
100%
Total Number
100%