Identification of gene regulatory networks by strategic gene disruptions and gene overexpressions
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Analysis of Gene Expression Data with Pathway Scores
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Predicting genetic regulatory response using classification
Bioinformatics
Inference of gene relations from microarray data by abduction
LPNMR'05 Proceedings of the 8th international conference on Logic Programming and Nonmonotonic Reasoning
Hierarchical multi-classification with predictive clustering trees in functional genomics
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
On the implementation of the CLP(BN) language
PADL'10 Proceedings of the 12th international conference on Practical Aspects of Declarative Languages
Explaining genetic knock-out effects using cost-based abduction
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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Determining the underlying regulatory mechanism of genetic networks is one of the central challenges of computational biology. Numerous methods have been developed and applied to the important but complex task of reverse engineering regulatory networks from high-throughput gene expression data. However, many challenges remain. In this paper, we are interested in learning rules that will reveal the causal genes for the expression variation from various relational data sources in addition to gene expression data. Following our previous work where we showed that time series gene expression data could potentially uncover causal effects, we describe an application of an inductive logic programming (ILP) system, to the task of identifying important regulatory relationships from discretized time series gene expression data, protein-protein interaction, protein phosphorylation and transcription factor data about the organism. Specifically, we learn rules for predicting gene expression levels at the next time step based on the available relational data and then generalize the learned theory to visualize a pruned network of important interactions. We evaluate and present experimental results on microarray experiments from Gasch et alon Saccharomyces cerevisiae.