Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Multi-Instance Learning Based Web Mining
Applied Intelligence
A Protein Interaction Verification System Based on a Neural Network Algorithm
CSBW '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference - Workshops
Multiple instance learning for labeling faces in broadcasting news video
Proceedings of the 13th annual ACM international conference on Multimedia
A proposed knowledge based approach for solving proteomics issues
CIBB'09 Proceedings of the 6th international conference on Computational intelligence methods for bioinformatics and biostatistics
Mathematical and Computer Modelling: An International Journal
Proceedings of the 12th International Workshop on Data Mining in Bioinformatics
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We propose a method for predicting types of protein-protein interactions using a multiple-instance learning (MIL) model. Given an interaction type to be predicted, the MIL model was trained using interaction data collected from biological pathways, where positive bags were constructed from interactions between protein complexes of that type, and negative bags from those of other types. In an experiment using the KEGG pathways and the Gene Ontology, the method successfully predicted an interaction type (phosphorylation) to an accuracy rate of 86.1%.