Mining from protein-protein interactions

Proteins are important cellular molecules, and interacting protein pairs provide biologically important information, such as functional relationships. We focus on the problem of predicting physically interacting protein pairs. This is an important problem in biology, which has been actively investigated in the field of data mining and knowledge discovery. Our particular focus is on data-mining-based methods, and the objective of this review is to introduce these methods for data mining researchers from technical viewpoints. We categorize those methods into three types: pairwise data-based, network-based, and integrative approaches, each approach being described in a different section. The first section is further divided into five types, such as supervised learning, algorithmic approaches, and unsupervised learning. The second section is mainly on link prediction, which can be further divided into two types, and two subsections that cover topics related with protein interaction networks are further added. The final section provides a wide variety of methods in integrative approaches. (c) 2012 Wiley Periodicals, Inc.