Discovering implicit associations among critical biological entities
Discovering implicit associations among critical biological entities
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DOI：
10.1504/ijdmb.2009.024846
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发表时间：
2009-05
影响因子：
0.3
通讯作者：
Kazuhiro Seki;Javed Mostafa
中科院分区：
文献类型：
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作者：
Kazuhiro Seki;Javed Mostafa
我们提出了一种基于推理网络预测隐式基因-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;一种基线使用关键字的共同注释，另一种利用自由文本。此外，我们探索使用领域本体来补充数据稀疏性，并检查全文文档的影响。所提出的框架的有效性证明了从现实世界的数据创建的基准数据集。
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.