Optimally orienting physical networks
RECOMB'11 Proceedings of the 15th Annual international conference on Research in computational molecular biology
Identifying a small set of marker genes using minimum expected cost of misclassification
Artificial Intelligence in Medicine
RECOMB'12 Proceedings of the 16th Annual international conference on Research in Computational Molecular Biology
k-Optimal: a novel approximate inference algorithm for ProbLog
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Sign assignment problems on protein networks
WABI'12 Proceedings of the 12th international conference on Algorithms in Bioinformatics
Large-Scale Signaling Network Reconstruction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Probabilistic Biological Network Alignment
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
PReach: Reachability in Probabilistic Signaling Networks
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Characterizing the Topology of Probabilistic Biological Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Motivation: The complex program of gene expression allows the cell to cope with changing genetic, developmental and environmental conditions. The accumulating large-scale measurements of gene knockout effects and molecular interactions allow us to begin to uncover regulatory and signaling pathways within the cell that connect causal to affected genes on a network of physical interactions. Results: We present a novel framework, SPINE, for Signaling-regulatory Pathway INferencE. The framework aims at explaining gene expression experiments in which a gene is knocked out and as a result multiple genes change their expression levels. To this end, an integrated network of protein–protein and protein-DNA interactions is constructed, and signaling pathways connecting the causal gene to the affected genes are searched for in this network. The reconstruction problem is translated into that of assigning an activation/repression attribute with each protein so as to explain (in expectation) a maximum number of the knockout effects observed. We provide an integer programming formulation for the latter problem and solve it using a commercial solver. We validate the method by applying it to a yeast subnetwork that is involved in mating. In cross-validation tests, SPINE obtains very high accuracy in predicting knockout effects (99%). Next, we apply SPINE to the entire yeast network to predict protein effects and reconstruct signaling and regulatory pathways. Overall, we are able to infer 861 paths with confidence and assign effects to 183 genes. The predicted effects are found to be in high agreement with current biological knowledge. Availability: The algorithm and data are available at http://cs.tau.ac.il/~roded/SPINE.html Contact: roded@post.tau.ac.il