Partial context similarity of gene/proteins in leukemia using context rank based hierarchical clustering algorithm.

Bano, Shahana; Rao, K. Rajasekara

Abstract

In this paper we propose a method that aims to reduce processing overheads by avoiding the need to choose between natural language processing tools such as part-of-speech taggers and parsers. Moreover, we suggest a structure for the immediate creation of a large-scale, annotated corpus with disease names, which can be applied to train our probabilistic model. In this proposed work, a context rank-based hierarchical clustering method is applied on different datasets relating to colon diseases, leukemia and mixed-lineage leukemia (MLL). An optimal rule-filtering algorithm is applied on these datasets to remove unwanted special characters for gene/protein identification. Finally, experimental results show that our proposed method outperformed existing methods in terms of time and clusters space.

Citation

BANO, S. and RAO, K.R. 2015. Partial context similarity of gene/proteins in leukemia using context rank based hierarchical clustering algorithm. International journal of electrical and computer engineering [online], 5(3), pages 483-490. Available from: http://doi.org/10.11591/ijece.v5i3.pp483-490

• Journal Article Type: Article
• Acceptance Date: Mar 16, 2015
• Online Publication Date: Jun 30, 2015
• Publication Date: Jun 30, 2015
• Deposit Date: Sep 22, 2023
• Publicly Available Date: Sep 22, 2023
• Journal: International journal of electrical and computer engineering
• Print ISSN: 2088-8708
• Electronic ISSN: 2722-2578
• Publisher: Institute of Advanced Engineering and Science
• Peer Reviewed: Peer Reviewed
• Volume: 5
• Issue: 3
• Pages: 483-490
• DOI: https://doi.org/10.11591/ijece.v5i3.pp483-490
• Keywords: Data clustering; Biomedical data; Gene sequencing; Protein sequencing; Leukemia; Machine learning
• Public URL: https://rgu-repository.worktribe.com/output/2064139