A supervised learning approach to the ensemble clustering of genes Online publication date: Wed, 02-Oct-2013
by Andrew K. Rider; Geoffrey Siwo; Scott J. Emrich; Michael T. Ferdig; Nitesh V. Chawla
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 9, No. 2, 2014
Abstract: High-throughput techniques have become a primary approach to gathering biological data. These data can be used to explore relationships between genes and guide development of drugs and other research. However, the deluge of data contains an overwhelming amount of unknown information about the organism under study. Therefore, clustering is a common first step in the exploratory analysis of high-throughput biological data. We present a supervised learning approach to clustering that utilises known gene-gene interaction data to improve results for already commonly used clustering techniques. The approach creates an ensemble similarity measure that can be used as input to any clustering technique and provides results with increased biological significance while not altering the clustering method.
Online publication date: Wed, 02-Oct-2013
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Mining and Bioinformatics (IJDMB):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com