Discovering implicit associations among critical biological entities. (5th May 2009)

• Record Type: Journal Article
• Authors: Seki, Kazuhiro; Mostafa, Javed
• Is Part Of: International journal of data mining and bioinformatics. Volume 3:Number 2(2009)
• Page Start: 105
• Page End: 123
• Publication Date: 2009-05-05
• Subjects: TDM -- text data mining -- literature-based discovery -- information retrieval -- inference networks -- free text -- full text -- domain ontology -- gene ontology -- MeSH -- bioinformatics -- critical entities -- implicit associations -- biological entities -- disease associations -- gene-disease associations -- gene functions -- phenotypes; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

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.