Sheela, T. and Lalitha, R. (2017) Statistical class prediction method for efficient microarray gene expression data sample classification. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 13-16 Sept. 2017, Udupi, India.
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Cancer classification is routinely done using gene expression data. With microarray technology, monitoring thousands of genes is an easy task. The reliable and precise classification of different tumour types is very important in cancer classification and drug discovery which is useful in providing better treatment. Microarray gene expression data analysis is extensively used for human cancer diagnosis and classification. Various methods of classification from the field of statistics and machine learning have been used to classify cancer microarray data. However, the large number of features with very few samples in the data is a challenge to the existing classification methods. In this work, our experiments are based on the statistical method of confidence interval for predicting sample class labels. Experiments show that the proposed method will achieve high classification accuracies with very few genes.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | biology computing;cancer;data analysis;genetics;learning (artificial intelligence);pattern classification;tumours;statistical method;sample class labels;statistical class prediction method;cancer classification;microarray technology;microarray gene expression data analysis;human cancer diagnosis;cancer microarray data;classification methods;tumour types;microarray gene expression data sample classification;Cancer;Support vector machines;Gene expression;Classification algorithms;Standards;Prediction methods;Tumors;Microarray Gene Expression data;Cancer Classification;High Dimensionality;Confidence Interval |
| Subjects: | D Physical Science > Computer Science |
| Divisions: | Department of > Computer Science |
| Depositing User: | C Swapna Library Assistant |
| Date Deposited: | 03 Jul 2019 05:52 |
| Last Modified: | 03 Jul 2019 05:52 |
| URI: | http://eprints.uni-mysore.ac.in/id/eprint/4549 |
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