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做好网络统计建设

Boosting statistical network inference by incorporating prior knowledge from multiple sources
课程网址: http://videolectures.net/mlsb2012_praveen_boosting/  
主讲教师: Paurush Praveen
开课单位: 波恩大学
开课时间: 2012-10-23
课程语种: 英语
中文简介:
统计学习方法,例如贝叶斯网络,已经从高通量实验中推断出细胞网络的高度普及。然而,实验数据中的固有噪声以及典型的低样本量限制了它们的性能,具有高误报率和漏报率。因此,将先前知识纳入学习过程已被确定为解决该问题的方法,并且已经设计了这样做的原理(Mukherjee&Speed,2008)。然而,迄今为止很少关注先验知识通常分布在多个异构知识源(例如GO,KEGG,HPRD等)中的事实。在这里,我们提出了两种方法来构建信息网络。多个知识来源:我们的第一个模型是使用贝叶斯推理的潜在因子模型。我们的第二个模型是Noisy OR模型,它假设整体先验是参与信息源的非确定性影响。将这两种模型与一种天真的方法进行比较,该方法假设知识来源独立。对人工创建的网络以及完整的KEGG途径的广泛模拟研究表明,与天真模型相比,两种建议方法都有显着改进。潜在因子模型的性能随着网络规模的增大而增加,而对于较小的网络,噪声OR模型看起来更优越。
课程简介: Statistical learning methods, such as Bayesian Networks, have gained a high popularity to infer cellular networks from high throughput experiments. However, the inherent noise in experimental data together with the typical low sample size limits their performance with high false positives and false negatives. Incorporating prior knowledge into the learning process has thus been identified as a way to address this problem, and principle a mechanism for doing so has been devised (Mukherjee & Speed, 2008). However, so far little attention has been paid to the fact that prior knowledge is typically distributed among multiple, heterogeneous knowledge sources (e.g. GO, KEGG, HPRD, etc.).\\ Here we propose two methods for constructing an informative network prior from multiple knowledge sources: Our first model is a latent factor model using Bayesian inference. Our second model is the Noisy-OR model, which assumes that the overall prior is a non-deterministic effect of participating information sources. Both models are compared to a naïve method, which assumes independence of knowledge sources. Extensive simulation studies on artificially created networks as well as full KEGG pathways reveal a significant improvement of both suggested methods compared to the naïve model. The performance of the latent factor model increases with larger network sizes, whereas for smaller networks the Noisy-OR model appears superior.
关 键 词: 统计学习; 贝叶斯网络; 因子模型; 非确定性影响
课程来源: 视频讲座网
最后编审: 2020-06-08:cxin
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