With the continuously growing literatures in the biomedical domain, it is not feasible for researchers to manually go through all information for answering questions. The task of making knowledge contained in texts in forms that machines can use for automated processing is more and more important. This paper describes a system to answer multiple-choice questions for the biomedical domain while reading a given document. In this study, we use the data from the pilot task "machine reading of biomedical texts about Alzheimer's disease" which is a task of the Question Answering for Machine Reading Evaluation (QA4MRE) Lab at CLEF 2012. We adapt the concept of answer validation that assumes the over-generation hypotheses will be checked in the validation step. In the following, the query expansion technique "global analysis" is applied. The best result is 0.51 c@1 score which is clearly above the baseline at CLEF 2012 and shows an exhilarating performance.