Abductive Stochastic Logic Programs for Metabolic Network Inhibition Learning
نویسندگان
چکیده
We revisit an application developed originally using Inductive Logic Programming (ILP) by replacing the underlying Logic Program (LP) description with Stochastic Logic Programs (SLPs), one of the underlying Probabilistic ILP (PILP) frameworks. In both the ILP and PILP cases a mixture of abduction and induction are used. The abductive ILP approach used a variant of ILP for modelling 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). The ILP approach learned logic models from non-probabilistic examples. The PILP approach applied in this paper is based on a general approach to introducing probability labels within a standard scientific experimental setting involving control and treatment data. Our results demonstrate that the PILP approach not only leads to a significant decrease in error accompanied by improved insight from the learned result but also provides a way of learning probabilistic logic models from probabilistic examples.
منابع مشابه
ProPPR: Efficient First-Order Probabilistic Logic Programming for Structure Discovery, Parameter Learning, and Scalable Inference
A key challenge in statistical relational learning is to develop a semantically rich formalism that supports efficient probabilistic reasoning using large collections of extracted information. This paper presents a new, scalable probabilistic logic called ProPPR, which further extends stochastic logic programs (SLP) to a framework that enables efficient learning and inference on graphs: using a...
متن کاملModelling Metabolic Pathways Using Stochastic Logic Programs-Based Bagging
In this paper we present a methodology to estimate rates of enzymatic reactions in metabolic pathways. Our methodology is based on applying stochastic logic learning in ensemble learning. Stochastic logic programs provide an efficient representation for metabolic pathways and ensemble methods give state-of-the-art performance and are useful for drawing biological inferences. We construct ensemb...
متن کاملEquivalence in Abductive Logic
We consider the problem of identifying equivalence of two knowledge bases which are capable of abductive reasoning. Here, a knowledge base is written in either first-order logic or nonmonotonic logic programming. In this work, we will give two definitions of abductive equivalence. The first one, explainable equivalence, requires that two abductive programs have the same explainability for any o...
متن کاملUniversitt a Degli Studi Di Bologna Deis Extensions of Logic Programming as Representation Languages for Machine Learning Extensions of Logic Programming as Representation Languages for Machine Learning
The representation language of Machine Learning has undergone a substantial evolution, starting from numerical descriptions to an attribute-value representations and nally to rst order logic languages. In particular, Logic Programming has recently been studied as a representation language for learning in the research area of Inductive Logic Programming. The contribution of this thesis is twofol...
متن کاملUsing Abduction for Induction of Normal Logic Programs
This paper proposes the approach of eXtended Hybrid Abductive Inductive Learning (XHAIL) for generalising positive and negative examples with respect to normal logic programs. A proof procedure is described that uses abduction to realise the abductive, deductive, and inductive phases which comprise this approach.
متن کامل