Modifying the K-medoid algorithm to improve gene expression data clustering using Biological Homogeneity Index (BHI) and Biological Stability Index (BSI)

Author

AUREUS JELLY

Date of Award

2013

Document Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Information Systems & Computer Science

First Advisor

Andrei D. Coronel, M.S.

Abstract

Clustering of genes on the basis of expression profiles is usually taken as a first step in understanding how a class of genes acts in consort during a biological process. The fundamental premise for applying such methods is that genes with similar functi

Comments

The C7.A784 2013

Recommended Citation

JELLY, AUREUS, (2013). Modifying the K-medoid algorithm to improve gene expression data clustering using Biological Homogeneity Index (BHI) and Biological Stability Index (BSI). Archīum.ATENEO.
https://archium.ateneo.edu/theses-dissertations/221