BMC Bioinformatics. 2014;15 Suppl 12(Suppl 12):S9. doi: 10.1186/1471-2105-15-S12-S9. Epub 2014 Nov 6.
The 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.
This 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.
Through 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.
The 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.
生物医学文献的数量呈爆炸式增长，但其中仍有大量有用信息未被发现。研究人员可以通过挖掘这些文献来提出有根据的生物医学假设。不幸的是，基于共现的流行挖掘方法会产生过多的目标概念，导致潜在目标概念的相关性排名下降。
本文提出了一种选择链接概念的新方法，该方法利用统计和文本特征来表示每个链接概念，然后将其分类为与起始概念相关或不相关。然后使用相关链接概念来发现目标概念。
通过评估，我们观察到文本特征提高了仅使用统计特征获得的结果。我们成功复制了 Swanson 的两个经典发现，并发现潜在相关目标概念的排名相对较高。
通过仅采用相关链接概念，大大减少了目标概念的数量，并提高了潜在相关目标概念的排名。因此，所提出的方法有可能帮助生物医学专家有效地找到最有用和最有价值的目标概念。
