A comparison of boundary graph grammars and context-free hypergraph grammars
Information and Computation
Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates
Mathematics and Computers in Simulation - IMACS sponsored Special issue on the second IMACS seminar on Monte Carlo methods
Stack-based algorithms for pattern matching on DAGs
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Fast and practical indexing and querying of very large graphs
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Efficient top-k aggregation of ranked inputs
ACM Transactions on Database Systems (TODS)
An error model for protein quantification
Bioinformatics
In silico identification of endo16 regulators in the sea urchin endomesoderm gene regulatory network
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
PANI: an interactive data-driven tool for target prioritization in signaling networks
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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In biological network analysis, the goal of the target identification problem is to predict molecule to inhibit (or activate) to achieve optimum efficacy and safety for a disease treatment. A related problem is the target prioritization problem which predicts a subset of molecules in a given disease-related network which contains successful drug targets with highest probability. Sensitivity analysis prioritizes targets in a dynamic network model using principled criteria, but fails to penalize off-target effects, and does not scale for large networks. We describe Pani (Putative TArget Nodes PrIoritization), a novel method that prunes and ranks the possible target nodes by exploiting concentration-time profiles and network structure (topological) information. Pani and two sensitivity analysis methods were applied to three signaling networks, mapk-pi3k; myosin light chain (mlc) phosphorylation and sea urchin endomesoderm gene regulatory network which are implicated for example in ovarian cancer; atrial fibrillation and deformed embryos. Predicted targets were compared against the molecules known to be targeted by drugs in clinical use for the respective diseases. Pani is orders of magnitude faster and prioritizes the majority of known targets higher than both sensitivity methods. This highlights a potential disagreement between absolute mathematical sensitivity and our intuition of influence. We conclude that empirical, structural methods like Pani, which demand almost no run time, offer benefits not available from quantitative simulation and sensitivity analysis.