Enhancing the accuracy of knowledge discovery: a supervised learning method

BMC Bioinformatics

Research

Feng Zhou¹  Zhihao Yang¹  Jian Wang¹  Liangxi Cheng¹  Hongfei Lin¹ 

[1] Information Retrieval Laboratory, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China;

关键词: Textual Feature;    MeSH;    MeSH Term;    Semantic Type;    Target Concept;   

DOI  :  10.1186/1471-2105-15-S12-S9

来源: Springer

【 摘 要 】

BackgroundThe amount of biomedical literature available is growing at an explosive speed, but a large amount of useful information remains undiscovered in it. Researchers can make informed biomedical hypotheses through mining this literature. Unfortunately, popular mining methods based on co-occurrence produce too many target concepts, leading to the declining relevance ranking of the potential target concepts.MethodsThis paper presents a new method for selecting linking concepts which exploits statistical and textual features to represent each linking concept, and then classifies them as relevant or irrelevant to the starting concepts. Relevant linking concepts are then used to discover target concepts.ResultsThrough an evaluation it is observed textual features improve the results obtained with only statistical features. We successfully replicate Swanson's two classic discoveries and find the rankings of potentially relevant target concepts are relatively high.ConclusionsThe number of target concepts is greatly reduced and potentially relevant target concepts gain higher ranking by adopting only relevant linking concepts. Thus, the proposed method has the potential to help biomedical experts find the most useful and valuable target concepts effectively.

【 授权许可】

Unknown   
© Cheng et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.