Data preparation and interannotator agreement: BioCreAtIvE task 1B.

Background: We prepared and evaluated training and test materials for an assessment of text mining methods in molecular biology. The goal of the assessment was to evaluate the ability of automated systems to generate a list of unique gene identifiers from PubMed abstracts for the three model organisms Fly, Mouse, and Yeast. This paper describes the preparation and evaluation of answer keys for training and testing.

Gene name identification and normalization using a model organism database.

Biology has now become an information science, and researchers are increasingly dependent on expert-curated biological databases to organize the findings from the published literature. We report here on a series of experiments related to the application of natural language processing to aid in the curation process for FlyBase. We focused on listing the normalized form of genes and gene products discussed in an article.

Evaluation of text data mining for database curation: lessons learned from the KDD Challenge Cup.

Motivation: The biological literature is a major repository of knowledge. Many biological databases draw much of their content from a careful curation of this literature. However, as the volume of literature increases, the burden of curation increases.

Rutabaga by any other name: extracting biological names.

As the pace of biological research accelerates, biologists are becoming increasingly reliant on computers to manage the information explosion. Biologists communicate their research findings by relying on precise biological terms; these terms then provide indices into the literature and across the growing number of biological databases. This article examines emerging techniques to access biological resources through extraction of entity names and relations among them.