Assigning roles to protein mentions: the case of transcription factors.
Transcription factors (TFs) play a crucial role in gene regulation, and providing structured and curated information about them is important for genome biology. Manual curation of TF related data is time-consuming and always lags behind the actual knowledge available in the biomedical literature. Here we present a machine-learning text mining approach for identification and tagging of protein mentions that play a TF role in a given context to support the curation process. More precisely, the method explicitly identifies those protein mentions in text that refer to their potential TF functions. The prediction features are engineered from the results of shallow parsing and domain-specific processing (recognition of relevant appearing in phrases) and a phrase-based Conditional Random Fields (CRF) model is used to capture the content and context information of candidate entities. The proposed approach for the identification of TF mentions has been tested on a set of evidence sentences from the TRANSFAC and FlyTF databases. It achieved an F-measure of around 51.5% with a precision of 62.5% using 5-fold cross-validation evaluation. The experimental results suggest that the phrase-based CRF model benefits from the flexibility to use correlated domain-specific features that describe the dependencies between TFs and other entities. To the best of our knowledge, this work is one of the first attempts to apply text-mining techniques to the task of assigning semantic roles to protein mentions.
School of Computer Science, University of Manchester, UK.
Hui
H
Yang
John
J
Keane
Casey M
CM
Bergman
Goran
G
Nenadic
D001185:Artificial Intelligence; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D010506:Periodicals as Topic; D015203:Reproducibility of Results; D014157:Transcription Factors
D014157:Transcription Factors