Much biological information is disseminated among scientists as unstructured text.
Extracting meaningful biological information from text using automated computational methods remains a significant bioinformatics challenge. This problem is important because large collections of text can reveal patterns not obvious through manual inspection. Thus, we have been exploring computational methods for dealing with the high-dimensionality of text data and assembling it into representations useful for providing biological insight. Specifically, we have looked at extracting content words from biology-related Medline abstracts using models based on Inverse Document Frequency and Residual Inverse Document Frequency. Similarly, we have also experimented with determining term importance based on comparative analyses of malaria and yeast literature.