New algorithm ranks scientific literature

Keeping up with current scientific literature is a daunting task, considering that hundreds to thousands of papers are published each day. Now researchers from North Carolina State University have developed a computer program to help them evaluate and rank scientific articles in their field.

The researchers use a text-mining algorithm to prioritize research papers to read and include in their Comparative Toxicogenomics Database (CTD), a public database that manually curates and codes data from the scientific literature describing how environmental chemicals interact with genes to affect human health.

To help select the most relevant papers for inclusion in the CTD, Thomas Wiegers, a research bioinformatician at NC State and the other co-lead author of the report, developed a sophisticated algorithm as part of a text-mining process. The application evaluates the text from thousands of papers and assigns a relevancy score to each document.

But how good is the algorithm at determining the best papers? To test that, the researchers text-mined 15 000 articles and sent a representative sample to their team of biocurators to manually read and evaluate on their own, blind to the computer’s score. The biocurators concurred with the algorithm 85 percent of the time with respect to the highest-scored papers.

Using the algorithm to rank papers allowed biocurators to focus on the most relevant papers, increasing productivity by 27 percent and novel data content by 100 percent.

There are always outliers in these types of experiments: occasions where the algorithm assigns a very high score to an article that a human biocurator quickly dismisses as irrelevant. The team that looked at those outliers was often able to see a pattern as to why the algorithm mistakenly identified a paper as important.

(The paper, “Text mining effectively scores and ranks the literature for improving chemical-gene-disease curation at the Comparative Toxicogenomics Database,” was published online April 17 in PLOS ONE. Co-authors are Dr. Cindy Murphy, a biocurator scientist at NC State; Dr. Carolyn Mattingly, associate professor of biology at NC State; and Drs. Robin Johnson, Jean Lay, Kelley Lennon-Hopkins, Cindy Saraceni-Richards and Daniela Sciaky from The Mount Desert Island Biological Laboratory.)