Evolutionary hypernetwork classifiers for protein-proteininteraction sentence filtering

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Evolutionary hypernetwork classifiers for protein-proteininteraction sentence filtering

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Genetic And Evolutionary Computation Conference archive
Proceedings of the 11th Annual conference on Genetic and evolutionary computation table of contents

Montreal, Québec, Canada

SESSION: Track 3: bioinformatics and computational biology table of contents

Pages 185-192

Year of Publication: 2009

ISBN:978-1-60558-325-9

Authors

Jakramate Bootkrajang	Seoul National University, Seoul, South Korea
Sun Kim	Seoul National University, Seoul, South Korea
Byoung-Tak Zhang	Seoul National University, Seoul, South Korea

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

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ABSTRACT

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.

REFERENCES

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