Name
Abstract | ||
|---|---|---|
We revisit an application developed originally using abductive Inductive Logic Programming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two Probabilistic ILP (PILP) approaches - abductive Stochastic Logic Programs (SLPs) and PRogramming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a PILP framework that provides possible worlds semantics to SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ILP, the PILP approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. Our results demonstrate that the PILP approach provides a way of learning probabilistic logic models from probabilistic examples, and the PILP models learned from probabilistic examples lead to a significant decrease in error accompanied by improved insight from the learned results compared with the PILP models learned from non-probabilistic examples. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/s10994-008-5076-4 | Machine Learning |
Keywords | Field | DocType |
Probabilistic inductive logic programming,Stochastic logic programs,Abduction,Probabilistic examples | Inductive logic programming,Knowledge representation and reasoning,Logic model,Computer science,Theoretical computer science,Artificial intelligence,Statistical model,Probabilistic logic,Stochastic programming,Machine learning,Semantics,Possible world | Journal |
Volume | Issue | ISSN |
73 | 1 | 0885-6125 |
ISBN | Citations | PageRank |
3-540-78468-3 | 6 | 0.54 |
References | Authors | |
16 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianzhong Chen | 1 | 21 | 3.68 |
| Stephen Muggleton | 2 | 3915 | 619.54 |
| Jose Santos | 3 | 11 | 1.37 |