An Information Entropy Based to Identify Dominant Species for No Biological Data Genes

Nurul Ain Nazirah, Shahreen Kasim, Dwiny Meidelfi

Abstract


This report discusses about the dominant species of no biological data genes. This genes are belong to animal species. Gene is one of a process where the biological data encoded in the gene that instructed by the DNA to convert into a functional product such as protein. In gene classification, with the growth of using gene expression database, there are not enough tools to extract the gene expression from these databases. There exist over 23,000 to 50,000 genes for animal genome. So, this might contribute to data redundancy as problems can happen while handling a huge database. Therefore, to overcome the problem, many approaches to cluster and determine the dominant species have been proposed in the previous literature. For this project, in order to determine the dominant species, the information entropy based method is used. As conclusion, the purpose of this research is to identify the dominant species of no biological genes using entropy method proposed.

Keywords


Genes; dominant species; clustering; entropy

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References


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Soltanian AR, Rabiei N, Bahreini F. Feature Selection in Microarray Data Using Entropy Information. In: Husi H, editor. Computational Biology [Internet]. Brisbane (AU): Codon Publications; 2019 Nov 21. Chapter 10. Available from: https://www.ncbi.nlm.nih.gov/books/NBK550347/ doi: 10.15586/computationalbiology.2019.ch10.

Soltanian AR, Rabiei N, Bahreini F. Feature Selection in Microarray Data Using Entropy Information. In: Husi H, editor. Computational Biology [Internet]. Brisbane (AU): Codon Publications; 2019 Nov 21. Chapter 10. Available from: https://www.ncbi.nlm.nih.gov/books/NBK550347/ doi: 10.15586/computationalbiology.2019.ch10.

Soltanian AR, Rabiei N, Bahreini F. Feature Selection in Microarray Data Using Entropy Information. In: Husi H, editor. Computational Biology [Internet]. Brisbane (AU): Codon Publications; 2019 Nov 21. Chapter 10. Available from: https://www.ncbi.nlm.nih.gov/books/NBK550347/ doi: 10.15586/computationalbiology.2019.ch10.




DOI: https://doi.org/10.30630/ijasce.2.3.101

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Organized / Collaboration

- Soft Computing and Data Mining Centre, UTHM, Malaysia and Department of Information Technology

- Society of Visual Informatics, Indonesia