Identifying gene regulatory networks from experimental data
RECOMB '99 Proceedings of the third annual international conference on Computational molecular biology
Using Bayesian networks to analyze expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Phase-independent rhythmic analysis of genome-wide expression patterns
Proceedings of the sixth annual international conference on Computational biology
A Maximum Entropy Algorithm for Rhythmic Analysis of Genome-Wide Expression Patterns
CSB '02 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Automated analysis of DNA hybridization images for high-throughput genomics
Machine Vision and Applications
TRICLUSTER: an effective algorithm for mining coherent clusters in 3D microarray data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Proceedings of the 2nd international workshop on Data and text mining in bioinformatics
Gene Specific Co-regulation Discovery: An Improved Approach
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
Cluster analysis on time series gene expression data
International Journal of Business Intelligence and Data Mining
Mining time-shifting co-regulation patterns from gene expression data
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Efficiently mining time-delayed gene expression patterns
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Mining time-delayed coherent patterns in time series gene expression data
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
ACSC '11 Proceedings of the Thirty-Fourth Australasian Computer Science Conference - Volume 113
Hi-index | 0.00 |
We introduce new methods for the analysis of short-term time-series data, and apply them to gene expression data in yeast. These include (1) methods for automated period detection in a predominately cycling data set and (2) phase detection between phase-shifted cyclic data sets. We show how to properly correct for the problem of comparing correlation coefficents between pairs of sequences of different lengths and small alphabets. In particular, we show that the correlation coefficient of sequences over alphabets of size two can exhibit very counter-intuitive behavior when compared with the Hamming distance. Finally, we address the predictability of known regulators via time-series analysis, and show that less than 20% of known regulatory pairs exhibit strong correlations in the Cho/Spellman data sets. By analyzing known regulatory relationships, we designed an edge detection function which identified candidate regulations with greater fidelity than standard correlation methods.