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代谢网络抑制学习的诱导随机逻辑程序
Abductive Stochastic Logic Programs for Metabolic Network Inhibition Learning |
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| 课程网址: |
http://videolectures.net/mlg07_santos_aslp/
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| 主讲教师: |
Jose Santos
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| 开课单位: |
伦敦帝国学院
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| 开课时间: |
2007-09-05
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| 课程语种: |
英语
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| 中文简介: |
我们重新访问最初使用Induc-开发的应用程序通过替换底层的Logic Pro-逻辑编程（ILP）使用随机逻辑程序（SLP）的克（LP）描述潜在的概率ILP（PILP）框架。在两个ILP中和PILP病例使用外展和诱导的混合物。该诱导性ILP方法使用ILP的变体来模拟抑制在代谢网络中。示例数据来源于研究核磁共振（NMR）对大鼠毒素的影响生物气体的时间跟踪分析以及背景知识代表京都基因和基因组百科全书的一个子集（KEGG）。 ILP方法从非概率学习逻辑模型例子。本文中应用的PILP方法基于在标准科学中引入概率标签的方法参与控制和治疗数据的实验设置。我们的结果表明PILP方法不仅导致了一个重要的信号 - 不能减少错误，同时提高学习的洞察力结果，但也提供了一种学习概率逻辑模型的方法概率例子。
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| 课程简介: |
We revisit an application developed originally using Induc-
tive Logic Programming (ILP) by replacing the underlying Logic Pro-
gram (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 e®ects of toxins on rats using Nuclear Magnetic Resonance (NMR)
time-trace analysis of their bio°uids 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 gen-
eral approach to introducing probability labels within a standard sci-
enti¯c experimental setting involving control and treatment data. Our
results demonstrate that the PILP approach not only leads to a signi¯-
cant decrease in error accompanied by improved insight from the learned
result but also provides a way of learning probabilistic logic models from
probabilistic examples.
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| 关 键 词: |
逻辑编程; 随机逻辑程序; 概率逻辑模型
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-06-30：cjy
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| 阅读次数: |
9
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