A two-step framework for inferring direct protein-protein interaction network from AP-MS data.

BACKGROUND: Affinity purification-mass spectrometry (AP-MS) has been widely used for generating bait-prey data sets so as to identify underlying protein-protein interactions and protein complexes. However, the AP-MS data sets in terms of bait-prey pairs are highly noisy, where candidate pairs contain many false positives. Recently, numerous computational methods have been developed to identify genuine interactions from AP-MS data sets. However, most of these methods aim at removing false positives that contain contaminants, ignoring the distinction between direct interactions and indirect interactions. RESULTS: In this paper, we present an initialization-and-refinement framework for inferring direct PPI networks from AP-MS data, in which an initial network is first generated with existing scoring methods and then a refined network is constructed by the application of indirect association removal methods. Experimental results on several real AP-MS data sets show that our method is capable of identifying more direct interactions than traditional scoring methods. CONCLUSIONS: The proposed framework is sufficiently general to incorporate any feasible methods in each step so as to have potential for handling different types of AP-MS data in the future applications.

Discriminative pattern mining and its applications in bioinformatics.

Discriminative pattern mining is one of the most important techniques in data mining. This challenging task is concerned with finding a set of patterns that occur with disproportionate frequency in data sets with various class labels. Such patterns are of great value for group difference detection and classifier construction. Research on finding interesting discriminative patterns in class-labeled data evolves rapidly and lots of algorithms have been proposed to specifically address this problem. Discriminative pattern mining techniques have proven their considerable value in biological data analysis. The archetypical applications in bioinformatics include phosphorylation motif discovery, differentially expressed gene identification, discriminative genotype pattern detection, etc. In this article, we present an overview of discriminative pattern mining and the corresponding effective methods, and subsequently we illustrate their applications to tackling the bioinformatics problems. In the end, we give a general discussion of potential challenges and future work for this task.