Abstract

Methods to improve text mining of molecular biology interactions are needed to capture a richer information space and qualify the quality of extraction. Simple interaction models fail to describe contextual and confidence information that would help with more fine-grained analyses. Herein a method is presented to streamline curation of text-mined data and a way to improve text mining of biomedical terms that can be adapted to other domains using different machine learning techniques. These advances can be integrated into more powerful text-mining systems to meet user demand and to further promote the adoption of text-mining tools. Additionally, three studies on the nature of biomedical publications are presented: their novelty hinges on the fact that each asks questions that had not been posed before. They cover the phenomena of retraction, ways to improve the impact of research, and the writing style used in biomedical literature. Retraction is a hot topic in recent times

Citations