Inferring characteristic phenotypes via class association rule mining in the bone dysplasia domain

Paul, Razan, Groza, Tudor, Hunter, Jane and Zankl, Andreas (2014) Inferring characteristic phenotypes via class association rule mining in the bone dysplasia domain. Journal of Biomedical Informatics, 48 73-83. doi:10.1016/j.jbi.2013.12.001


Author Paul, Razan
Groza, Tudor
Hunter, Jane
Zankl, Andreas
Title Inferring characteristic phenotypes via class association rule mining in the bone dysplasia domain
Journal name Journal of Biomedical Informatics   Check publisher's open access policy
ISSN 1532-0464
1532-0480
Publication date 2014-04
Year available 2014
Sub-type Article (original research)
DOI 10.1016/j.jbi.2013.12.001
Open Access Status
Volume 48
Start page 73
End page 83
Total pages 11
Place of publication Maryland Heights, MO United States
Publisher Academic Press
Collection year 2015
Language eng
Abstract Finding, capturing and describing characteristic features represents a key aspect in disorder definition, diagnosis and management. This process is particularly challenging in the case of rare disorders, due to the sparse nature of data and expertise. From a computational perspective, finding characteristic features is associated with some additional major challenges, such as formulating a computationally tractable definition, devising appropriate inference algorithms or defining sound validation mechanisms. In this paper we aim to deal with each of these problems in the context provided by the skeletal dysplasia domain. We propose a clear definition for characteristic phenotypes, we experiment with a novel, class association rule mining algorithm and we discuss our lessons learned from both an automatic and human-based validation of our approach.
Keyword Class association rule mining
Mining characteristic phenotypes
Bone dysplasias
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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