Authors: Diego Liberati
This paper attempts to cluster leukemia patients described by gene expression data, and to discover the most discriminating genes that are responsible for the clustering. A combined approach of Principal Direction Divisive Partitioning and bisect K-means algorithms is applied to the clustering of the investigated leukemia dataset. Both unsupervised and supervised methods are considered in order to get optimal result. The combination of PDDP and bisect K-means successfully clusters leukemia patients, and efficiently discovers salient genes able to the discriminate the clusters. The combined approach works well on the automatic clustering of leukemia patients depending merely on the gene expression information, and it has great potential on solving similar problems, like classifying pancreatic tumors. The salient identified genes may thus enhance relevant information for discriminating among leukemias.
Comments: 35 Pages.
[v1] 2017-09-19 06:50:53
Unique-IP document downloads: 25 times
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