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Oren Shiran

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2 papers

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AIIM Journal 2006 Journal Article

Constructing explanatory process models from biological data and knowledge

Pat Langley
Oren Shiran
Jeff Shrager
Ljupčo Todorovski
Andrew Pohorille

Objective We address the task of inducing explanatory models from observations and knowledge about candidate biological processes, using the illustrative problem of modeling photosynthesis regulation. Methods We cast both models and background knowledge in terms of processes that interact to account for behavior. We also describe IPM, an algorithm for inducing quantitative process models from such input. Results We demonstrate IPM’s use both on photosynthesis and on a second domain, biochemical kinetics, reporting the models induced and their fit to observations. Conclusion We consider the generality of our approach, discuss related research on biological modeling, and suggest directions for future work.

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AAAI Conference 2005 Conference Paper

Inducing Hierarchical Process Models in Dynamic Domains

Ljupco Todorovski
Oren Shiran

Research on inductive process modeling combines background knowledge with time-series data to construct explanatory models, but previous work has placed few constraints on search through the model space. We present an extended formalism that organizes process knowledge in a hierarchical manner, and we describe HIPM, a system that carries out constrained search for hierarchical process models. We report experiments that suggest this approach produces more accurate and plausible models with less effort. We conclude by discussing related research and directions for future work.

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