A New Profile Alignment Method for Clustering Gene Expression Data

We focus on clustering gene expression temporal profiles, and propose a novel, simple algorithm that is powerful enough to find an efficient distribution of genes over clusters. We also introduce a variant of a clustering index that can effectively decide upon the optimal number of clusters for a given dataset. The clustering method is based on a profilealignment approach, which minimizes the mean-square-error of the first order differentials, to hierarchically cluster microarray time-series data. The effectiveness of our algorithm has been tested on datasets drawn from standard experiments, showing that our approach can effectively cluster the datasets based on profile similarity.

• Type: Conference
• Year: 2006
• Where: AI
• Authors: Ataul Bari, Luis Rueda
• Keywords: AI 2006, Artificial Intelligence, Clustering Index, Expression Temporal Profiles, Microarray Time-series Data