Seki Kazuhiro, Mostafa Javed
Graduate School of Science and Technology, Kobe University, 1-1 Rokkodai, Nada, Kobe 657-8501, Japan.
Pac Symp Biocomput. 2007:316-27.
We propose an approach to predicting implicit gene-disease associations based on the inference network, whereby genes and diseases are represented as nodes and are connected via two types of intermediate nodes: gene functions and phenotypes. To estimate the probabilities involved in the model, two learning schemes are compared; one baseline using co-annotations of keywords and the other taking advantage of free text. Additionally, we explore the use of domain ontologies to complement data sparseness and examine the impact of full text documents. The validity of the proposed framework is demonstrated on the benchmark data set created from real-world data.
我们提出了一种基于推理网络预测隐性基因-疾病关联的方法，其中基因和疾病被表示为节点，并通过两种类型的中间节点连接：基因功能和表型。为了估计模型中涉及的概率，比较了两种学习方案；一种是使用关键词共注释的基线方法，另一种是利用自由文本的方法。此外，我们探索使用领域本体来补充数据稀疏性，并研究全文文档的影响。在从真实世界数据创建的基准数据集上证明了所提出框架的有效性。
