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The improvement of accuracy of gene expression data classification with gene ontology
| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Qofrani, E. Jalali, M. Kalani, M.R. |
| Copyright Year | 2014 |
| Description | Author affiliation: Informatic Educ. Group of Med. Sci. of Mashhad, Mashhad, Iran (Kalani, M.R.) || Mashhad Branch, Islamic Azad Univ., Mashhad, Iran (Jalali, M.) || Imam Reza Int. Univ., Mashhad, Iran (Qofrani, E.) |
| Abstract | Gene selection is one of important research issues in analysis of gene expression data classification. Current methods try to reduce genes by means of statistical calculations and have used semantic similarity under gene ontology. In this article a technique has been presented based on which in addition to considering biological relation among genes, redundant genes by means of hierarchical clustering are omitted and the accuracy of classification increases. The structure and function of this technique have also been explained. The experiments using a single real data set indicate that the proposed technique in addition to selecting fewer genes, have higher accuracy of classification (Loocv), comparing to the technique that is based on semantic similarity. |
| Starting Page | 1 |
| Ending Page | 5 |
| File Size | 332332 |
| Page Count | 5 |
| File Format | |
| e-ISBN | 9781479980215 |
| DOI | 10.1109/ICTCK.2014.7033532 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-11-26 |
| Publisher Place | Iran |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Accuracy Correlation Ontology Semantic Similarity Semantics Clustering algorithms Ontologies Classification of Gene Expression Data Classification algorithms Gene expression Gene Selection |
| Content Type | Text |
| Resource Type | Article |
