Logo image
Back
Mining hidden connections among biomedical concepts from disjoint biomedical literature sets through semantic‐based association rule
Journal article   Open access   Peer reviewed

Mining hidden connections among biomedical concepts from disjoint biomedical literature sets through semantic‐based association rule

Xiaohua Hu, Xiaodan Zhang, Illhoi Yoo, Xiaofeng Wang and Jiali Feng
International journal of intelligent systems, v 25(2), pp 207-223
Feb 2010
DOI: https://doi.org/10.1002/int.20396
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1002/int.20396View
Published, Version of Record (VoR)Open Access (License Unspecified),  Open

Abstract

The novel connection between Raynaud disease and fish oils was uncovered from two disjointed biomedical literature sets by Swanson in 1986. Since then, there have been many approaches to uncover novel connections by mining the biomedical literature. One of the popular approaches is to adapt the association rule (AR) method to automatically identify implicit novel connections between concept A and concept C from two disjointed sets of documents through intermediate B concept. Since A and C concepts do not occur together in the same data set, the mining goal is to find novel connection among A and C concepts in the disjoint data sets. It first applies association rule to the two disjointed biomedical literature sets separately to generate two rule sets (A→B, B→C), and then applies transitive law to get the novel connections A→C. However, this approach generates a huge number of possible connections among the millions of biomedical concepts and a lot of these hypothetical connections are spurious, useless, and/or biologically meaningless. Thus it is essential to develop new approach to generate highly likely novel and biologically relevant connections among the biomedical concepts. This paper presents a biomedical semantic‐based association rule system (Bio‐SARS) that significantly reduce spurious/useless/biologically irrelevant connections through semantic filtering. Compared to other approaches such as latent semantic indexing and traditional association rule‐based approach, our approach generates much fewer rules and a lot of these rules represent relevant connections among biological concepts. © 2009 Wiley Periodicals, Inc.

Metrics

5 Record Views
34 citations in Web of Science
35 citations in Scopus

Details

UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Computer Science, Artificial Intelligence
Logo image