Marinka Zitnik

Fusing bits and DNA

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BMC Bioinformatics: Extracting Gene Regulatory Networks from Text

Our paper on Sieve-based relation extraction of gene regulatory networks from biological literature has been published in BMC Bioinformatics.

In the paper, we describe a network extraction algorithm, which is an improvement on our winning submission to BioNLP 2013. Our method is designed as a sieve-based system and uses linear-chain conditional random fields and rules for relation extraction. To enable extraction of distant relations we transform the data into skip-mention sequences. We then infer multiple models, each of which is able to extract a particular relationship type (e.g., inhibition, activation, binding). Further analysis following the challenge showed that all relation extraction sieves contribute to the predictive performance of the proposed approach. Also, features constructed by considering mention words and their prefixes and suffixes are the most important features for higher accuracy of extraction. The analysis also showed that our choice of transforming data into skip-mention sequences is appropriate for detecting relations between distant mentions.