Automatic Choice of Control Measurements

Abstract. In experimental design, a standard approach for distinguishing experimentally induced effects from unwanted effects is to design control measurements that differ only in terms of the former. However, in some cases, it may be problematic to design and measure controls specifically for an experiment. In this paper, we investigate the possibility of learning to choose suitable controls from a database of potential controls, which differ in their degree of relevance to the experiment. This approach is especially relevant in the field of bioinformatics where experimental studies are predominantly small-scale, while vast amounts of biological measurements are becoming increasingly available. We focus on finding controls for differential gene expression studies (case vs control) of various cancers. In this situation, the ideal control would be a healthy sample from the same tissue (the same mixture of cells as the tumor tissue), under the same conditions except for cancer-sp...

• Type: Conference
• Year: 2009
• Where: ACML
• Authors: Gayle Leen, David R. Hardoon, Samuel Kaski
• Keywords: ACML 2009, Control Measurements, Gene Expression, Gene Expression Studies, Machine Learning