A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
Modelling uncertainty in transcriptome measurements enhances network component analysis of yeast metabolic cycle
2009
2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Using high throughput DNA binding data for transcription factors and DNA microarray time course data, we constructed four transcription regulatory networks and analysed them using a novel extension to the network component analysis (NCA) approach. We incorporated probe level uncertainties in gene expression measurements into the NCA analysis by the application of probabilistic principal component analysis (PPCA), and applied the method to data from yeast metabolic cycle. Analysis shows
doi:10.1109/icassp.2009.4959947
dblp:conf/icassp/ChangHN09
fatcat:5525rmrtbfcevnd6ynsjhdqc34