Analyzing the Information Content of Text-Based Files in Supplementary Materials of Biomedical Literature

Abstract

We present an analysis of supplementary materials of PubMed Central (PMC) articles and show their importance in indexing and searching biomedical literature, in particular for the emerging genomic medicine field. On a subset of articles from PubMed Central, we use text mining methods to extract MeSH terms from abstracts, full texts, and text-based supplementary materials. We find that the recall of MeSH annotations increases by about 5.9 percentage points (+20% on relative percentage) when considering supplementary materials compared to using only abstracts. We further compare the supplementary material annotations with full-text annotations and we find out that the recall of MeSH terms increases by 1.5 percentage point (+3% on relative percentage). Additionally, we analyze genetic variant mentions in abstracts and full-texts and compare them with mentions found in supplementary text-based files. We find that the majority (about 99%) of variants are found in text-based supplementary files. In conclusion, we suggest that supplementary data should receive more attention from the information retrieval community, in particular in life and health sciences.

Keywords: Semantic annotation; Supplementary materials; Text mining.