title: New cluster ensemble approach to integrative biological data analysis.
abstract: Clinical data has been employed as the major factor for traditional cancer prognosis. However, this classic approach may be ineffective for analysing morphologically indistinguishable tumour subtypes. As such, microarray technology emerges as the promising alternative. Despite a large number of microarray studies, the actual clinical application of gene expression data analysis remains limited owing to the complexity of generated data and the noise level. Recently, the integrative cluster analysis of both clinical and gene expression data has been shown to be an effective alternative to overcome the above-mentioned problems. This paper presents a novel method for using cluster ensembles that is accurate for analysing heterogeneous biological data. Evaluation against real biological and benchmark data sets suggests that the quality of the proposed model is higher than many state-of-the-art cluster ensemble techniques and standard clustering algorithms.
authors.affiliation: School of Information Technology, Mae Fah Luang University, 57100, Thailand. nt.iamon@gmail.com
authors.forename: Natthakan
authors.initials: N
authors.lastname: Iam-On
authors.forename: Tossapon
authors.initials: T
authors.lastname: Boongoen
authors.forename: Simon
authors.initials: S
authors.lastname: Garrett
authors.forename: Chris
authors.initials: C
authors.lastname: Price
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D009369:Neoplasms / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated / Q000379:methods