Knowledge extraction for assisted curation of summaries of bacterial transcription factor properties

AbstractTranscription factors (TFs) play a main role in transcriptional regulation of bacteria, as they regulate transcription of the genetic information encoded in DNA. Thus, the curation of the properties of these regulatory proteins is essential for a better understanding of transcriptional regulation. However, traditional manual curation of article collections to compile descriptions of TF properties takes significant time and effort due to the overwhelming amount of biomedical literature, which increases every day. The development of automatic approaches for knowledge extraction to assist curation is therefore critical. Here, we show an effective approach for knowledge extraction to assist curation of summaries describing bacterial TF properties based on an automatic text summarization strategy. We were able to recover automatically a median 77% of the knowledge contained in manual summaries describing properties of 177 TFs ofEscherichia coli K-12 by processing 5961 scientific articles. For 71% of the TFs, our approach extracted new knowledge that can be used to expand manual descriptions. Furthermore, as we trained our predictive model with manual summaries ofE. coli, we also generated summaries for 185 TFs ofSalmonella enterica serovar Typhimurium from 3498 articles. According to the manual curation of 10 of theseSalmonella typhimurium summaries, 96% of their sentences contained relevant knowledge. Our results demonstrate the feasibility to assist manual curation to ex...
Source: Database : The Journal of Biological Databases and Curation - Category: Databases & Libraries Source Type: research