A gravity inspired clustering algorithm for gene selection from high-dimensional microarray data

ABSTRACT

The problem of selecting a subset of genes relevant to a particular disease classification task from high-dimensional gene microarray data has been an area of active research in the scientific community. A wide variety of methods ranging from assigning scores to genes based on their interactions with the output to the Gravity inspired clustering algorithm with the hybrid approach have been proposed. The feature sets selected by this approach have been evaluated by using the Support Vector Machine (SVM) with Leave One Out Cross Validation (LOOCV). The method has been evaluated on twelve different publicly available microarray cancer datasets and the proposed method provided good accuracy of 90.16% to the colon dataset, 98.59% to the ALL-AML dataset, 97.03% to the lung cancer dataset, 93.07% to the prostate cancer dataset, 98.41% to the meningioma dataset, 83.72% to the brain tumour dataset and has been shown to compete with existing methods.

KEYWORDS:

• Gravity inspired clustering algorithm
• support vector machine
• leave one out cross validation
• DNA (Deoxyribonucleic Acid)
• high dimensional gene microarray data
• coarse-grained feature selection
• fine-grained feature selection
• Otsu Thresholding

Disclosure statement

No potential conflict of interest was reported by the authors.