Mining the relation between leukemia and genes using Weka
10.3969/j.issn.1671-3982.2015.01.011
- VernacularTitle:利用Weka挖掘白血病与基因的关系
- Author:
Rui HUANG
;
Lei YAN
- Publication Type:Journal Article
- Keywords:
Weka;
Cobweb;
Cluster analysis;
Leukemia;
Gene;
Data mining;
Coocurence mining system;
Visual analysis;
Research hot spot
- From:Chinese Journal of Medical Library and Information Science
2015;(1):50-54,60
- CountryChina
- Language:Chinese
-
Abstract:
Objective To mine the relation between leukemia and genes using Weka. Methods The papers on leuke-mia and genes were retrieved from PubMed, their subject headings and subheadings were extracted using BICOMB to generate co-occurrence matrix and term-paper matrix. The research hotspots were found by cluster analysis of the data on co-occurrence matrix using Weka and Cobweb. The literature was verified. Results The 42 high fre-quency words were clustered into 7 classes by Weka. No high frequency words of leukemia or genes were found in classes 1, 2, 4 and 5, indicating that their clustering efficiency was poor. The clustering efficiency of the other 3 classes was good. Conclusion Cluster analysis showed that leukemia is related with myc gene, ab1 gene, p53 gene, virus gene, immunoglobulin gene and mdm gene.
