Nala: text mining natural language mutation mentions.

ABSTRACT:

Motivation

The extraction of sequence variants from the literature remains an important task. Existing methods primarily target standard (ST) mutation mentions (e.g. 'E6V'), leaving relevant mentions natural language (NL) largely untapped (e.g. 'glutamic acid was substituted by valine at residue 6').

Results

We introduced three new corpora suggesting named-entity recognition (NER) to be more challenging than anticipated: 28-77% of all articles contained mentions only available in NL. Our new method nala captured NL and ST by combining conditional random fields with word embedding features learned unsupervised from the entire PubMed. In our hands, nala substantially outperformed the state-of-the-art. For instance, we compared all unique mentions in new discoveries correctly d

SUBMITTER: Cejuela JM 

PROVIDER: S-EPMC5870606 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature