Mining Cross-Graph Quasi-Cliques in Gene Expression and Protein Interaction Data
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Modeling Multiple Time Units Delayed Gene Regulatory Network Using Dynamic Bayesian Network
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
A skip-chain conditional random field for ranking meeting utterances by importance
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
Learning the structure of dynamic probabilistic networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
FusGP: bayesian co-learning of gene regulatory networks and protein interaction networks
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Hi-index | 0.00 |
Inference of Gene Regulatory Networks (GRN) is important in understanding signal transduction pathways. This involves predicting the correct sequence of interactions and identifying all interacting genes. Using only gene expression data is insufficient, so additional sources of data like protein-protein interaction network (PPIN) are required. In this paper, we model time delayed interactions using a skip-chain model which finds missing edges between non-consecutive time points based on PPIN. Highest Viterbi approximation is used to select skip-edges. The k-skip validation model checks for kmissing genes between a predicted interaction, giving us advantages of validation as well as expansion of GRN. The method is demonstrated on a cell-division cycle data of S.cerevisiae(yeast). Comparison of the present method, with a previous approach of modeling PPIN by using a Gibbs prior are given.