Automatic Extraction of Biological Information from Scientific Text: Protein-Protein Interactions
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Optimizing syntax patterns for discovering protein-protein interactions
Proceedings of the 2005 ACM symposium on Applied computing
A comparison of alternative parse tree paths for labeling semantic roles
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Negation of protein–protein interactions
Bioinformatics
A tree kernel-based method for protein-protein interaction mining from biomedical literature
KDLL'06 Proceedings of the 2006 international conference on Knowledge Discovery in Life Science Literature
Mining protein interaction from biomedical literature with relation kernel method
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
DNA hypernetworks for information storage and retrieval
DNA'06 Proceedings of the 12th international conference on DNA Computing
Hypernetworks: A Molecular Evolutionary Architecture for Cognitive Learning and Memory
IEEE Computational Intelligence Magazine
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Protein-Protein Interaction (PPI) extraction, among ongoing biomedical text mining challenges, is becoming a topic in focus because of its crucial role in providing a starting point to understand biological processes. Machine learning (ML) techniques have been applied to extract the PPI information from biomedical literature. Although they have provided reasonable performance so far, more features are required for real use. In particular, many ML-approaches lack human understandability for learned models. Here, we propose a novel method for classifying PPI sentences. Our approach utilizes the modified hypernetwork model, a hypergraph with weighted hyperedges that are calibrated via an evolutionary learning method. The evolutionary hypernetwork memorizes fragments of training patterns while self-adjusting its own structure for detecting PPI sentences. For experiments, we show that our approach provides competitive performance compared to other ML methods. Apart from its superior classification performance, the evolving hypernetwork model comes with a highly interpretable structure. We show how significant PPI patterns can be naturally extracted from the learned model. We also analyze the discovered patterns.