Modeling and analysis of gene expression time-series based on co-expression.
In this paper a novel approach is introduced for modeling and clustering gene expression time-series. The radial basis function neural networks have been used to produce a generalized and smooth characterization of the expression time-series. A co-expression coefficient is defined to evaluate the similarities of the models based on their temporal shapes and the distribution of the time points. The profiles are grouped using a fuzzy clustering algorithm incorporated with the proposed co-expression coefficient metric. The results on artificial and real data are presented to illustrate the advantages of the metric and method in grouping temporal profiles. The proposed metric has also been compared with the commonly used correlation coefficient under the same procedures and the results show that the proposed method produces better biologically relevant clusters.
Möller-Levet
Carla S
CS
School of Electrical and Electronic Engineering, The University of Manchester, Manchester, M60 IQD, United Kingdom. c.moller-levet@postgrad.manchester.ac.uk
Yin
Hujun
H
D000465:Algorithms; D000818:Animals; D002453:Cell Cycle / Q000502:physiology; D016000:Cluster Analysis; D003198:Computer Simulation; D015870:Gene Expression; D006801:Humans; D008957:Models, Genetic; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis; D012680:Sensitivity and Specificity; D013997:Time Factors; D015003:Yeasts / Q000235:genetics