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.

中文翻译：

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从概率示例中学习概率逻辑模型


我们重新审视最初使用溯因归纳逻辑编程（ILP）开发的应用程序，用于模拟代谢网络中的抑制。示例数据源自毒素对大鼠影响的研究，使用核磁共振 (NMR) 对其生物体液进行时间追踪分析，并结合代表京都基因和基因组百科全书 (KEGG) 子集的背景知识。我们现在将两种概率 ILP (PILP) 方法——溯因随机逻辑程序 (SLP) 和统计建模编程 (PRISM) 应用于应用程序。两种方法都支持溯因学习和概率预测。溯因 SLP 是一个 PILP 框架，它通过溯因向 SLP 提供可能的世界语义。本文应用的 PILP 方法不是像 ILP 那样从非概率示例中学习逻辑模型，而是基于在涉及控制和处理数据的标准科学实验设置中引入概率标签的通用技术。我们的结果表明，PILP 方法提供了一种从概率示例中学习概率逻辑模型的方法，并且与从非非概率示例中学习的 PILP 模型相比，从概率示例中学习的 PILP 模型可以显着减少错误，同时提高对学习结果的洞察力。 - 概率示例。