Computational analysis of complex traits: text- and network- and phenotype-mining

Andrey Rzhetsky
University of Chicago
Biomedical Informatics

The apparent tremendous information overload in molecular biology is a mere example of the status common to all fields of the current science and
culture: An ever-strengthening avalanche of novel data and ideas overwhelms specialists and non-specialists alike and makes enormous chunks of knowledge invisible/inaccessible to those who desperately need it.

The help of relieving the information overload may come from the text-miners who can automatically extract and catalogue facts described in books and journals. Additional layer of modeling can help to superimpose text-mined data with ³raw² experimental measurements.

My talk will touch the following questions: What is text-mining? In what ways is text-mining useful (if at all)? How do mathematical models help us to differentiate true and false statements in literature? How will text-mining help us to find cures for human and non-human maladies? How text-mined data can be used in combination with non-textual data?

Back to Workshop IV: Search and Knowledge Building for Biological Datasets