New challenges for text mining: mapping between text and manually curated pathways

Background: Associating literature with pathways poses new challenges to the Text Mining (TM) community. There are three main challenges to this task: (1) the identification of the mapping position of a specific entity or reaction in a given pathway, (2) the recognition of the causal relationships among multiple reactions, and (3) the formulation and implementation of required inferences based on biological domain knowledge. Results: To address these challenges, we constructed new resources to link the text with a model pathway; they are: the GENIA pathway corpus with event annotation and NF-kB pathway. Through their detailed analysis, we address the untapped resource, `bio-inference,' as well as the differences between text and pathway representation. Here, we show the precise comparisons of their representations and the nine classes of `bio-inference' schemes observed in the pathway corpus. Conclusions: We believe that the creation of such rich resources and their detailed...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Kanae Oda, Jin-Dong Kim, Tomoko Ohta, Daisuke Okanohara, Takuya Matsuzaki, Yuka Tateisi, Jun-ichi Tsujii
• Keywords: BMCBI 2008, GENIA Pathway Corpus, Pathway Corpus, Text Mining