Abstract
The affinity purification mass spectrometry (AP-MS) has become one of the most important experimental methods for detecting and characterizing protein–protein interactions (PPIs). In recent years, a large number of AP-MS data sets have been generated. To construct the interactome networks, the development of computational methods for effectively analyzing such data is highly demanded. In this chapter, we first present the basic process for AP-MS data generation. Then, we discuss the PPI prediction problem from two different pattern mining viewpoints: correlation mining and discriminative pattern mining. In addition, we present two different validation methods for assessing predicted PPIs and conclude with some open problems in this field.
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