Biomedical Hypothesis Generation and Testing by Evolutionary Computation.

Filtering the immense amount of data available electronically over the World Wide Web is an important task of search engines in data mining applications. Users when performing search often formulate hypotheses that they want to find supporting data for. The initial hypothesis reflects their preliminary knowledge of the subject. The final hypotheses at the end of the search reflect what they learned about a given subject and reflect the supporting information they found during search. We propose an evolutionary computation-based method that automatically generates queries and retrieves information to prove or disprove a given hypothesis. In case there is no supporting data, the system evolves another hypothesis for which it can find supporting data. We show preliminary results obtained for hypotheses related to plague where the data used is the Entrez PubMed data set.