A linear constraint satisfaction approach to cost-based abduction
Artificial Intelligence
Cost-based abduction and MAP explanation
Artificial Intelligence
Finding MAPs for belief networks is NP-hard
Artificial Intelligence
Polynomial solvability of cost-based abduction
Artificial Intelligence
Artificial Intelligence - Special issue: artificial intelligence research in Japan
Approximating MAPs for belief networks is NP-hard and other theorems
Artificial Intelligence
An algorithm for finding MAPs for belief networks through cost-based abduction
Artificial Intelligence
Abductive reasoning with recurrent neural networks
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Generalized chart algorithm: an efficient procedure for cost-based abduction
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Neural Networks - 2005 Special issue: IJCNN 2005
Finding MAPs Using High Order Recurrent Networks
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
Probabilistic inductive logic programming: theory and applications
Probabilistic inductive logic programming: theory and applications
Probabilistic semantics for cost based abduction
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Hi-index | 0.00 |
Cost-Based Abduction (CBA) is an AI model for reasoning under uncertainty. In CBA, evidence to be explained is treated as a goal which is true and must be proven. Each proof of the goal is viewed as a feasible explanation and has a cost equal to the sum of the costs of all hypotheses that are assumed to complete the proof. The aim is to find the Least Cost Proof. This paper uses CBA to develop a novel method for modeling Genetic Regulatory Networks (GRN) and explaining genetic knock-out effects. Constructing GRN using multiple data sources is a fundamental problem in computational biology. We show that CBA is a powerful formalism for modeling GRN that can easily and effectively integrate multiple sources of biological data. In this paper, we use three different biological data sources: Protein-DNA, Protein-Protein and gene knock-out data. Using this data, we first create an un-annotated graph; CBA then annotates the graph by assigning a sign and a direction to each edge. Our biological results are promising; however, this manuscript focuses on the mathematical modeling of the application. The advantages of CBA and its relation to Bayesian inference are also presented.