Figure 2
Among the 10,000 random articles, the articles with at least one mentioned mutation are selected (using tmVar 2.0). We compare the performances of two different approaches for detecting the variant-relevant articles. The first approach identifies articles that mention any disease or gene or any of their synonym in their titles and abstracts. In the second approach, we only search for the articles that mention the variant-relevant keywords in their full-body text. The variant-relevant keywords is a weighted list of the words that appear frequently in a set of 10,000 random articles with at least one mentioned variants (using tmVar 2.0). Subsequently, an article is considered to be relevant to variants if at least 10% of these variant-relevant keywords are appear in the full-body text. The number of variants that are found in the articles selected by the first approach and the second approach are 5,760 and 6,087, respectively. The number of variants identified by both approaches is 5,476. The number of variants that are only found by the first approach is 284, of which 97% are false positive (unrelated text wrongly identified as a variant). The number of variants that are only found by the second approach is 611, of which only 10% are false positive. These results show that the second approach which is based on the variant-relevant keywords outperforms the first approach.
