Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Multi-way relation classification: application to protein-protein interactions
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Information Processing and Management: an International Journal
Kernel approaches for genic interaction extraction
Bioinformatics
A graph kernel for protein-protein interaction extraction
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
A rich feature vector for protein-protein interaction extraction from multiple corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
An Overview of BioCreative II.5
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Simplicity is better: revisiting single kernel PPI extraction
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Entity-focused sentence simplification for relation extraction
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
A hybrid approach to extract protein–protein interactions
Bioinformatics
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Extraction of protein-protein interactions from scientific papers is a relevant task in the biomedical field. Machine learning-based methods such as kernel-based represent the state-of-the-art in this task. Many efforts have focused on obtaining new types of kernels in order to employ syntactic information, such as parse trees, to extract interactions from sentences. These methods have reached the best performances on this task. Nevertheless, parse trees were not exploited by other machine learning-based methods such as Bayesian networks. The advantage of using Bayesian networks is that we can exploit the structure of the parse trees to learn the Bayesian network structure, i.e., the parse trees provide the random variables and also possible relations among them. Here we use syntactic relation as a causal dependence between variables. Hence, our proposed method learns a Bayesian network from parse trees. The evaluation was carried out over five protein-protein interaction benchmark corpora. Results show that our method is competitive in comparison with state-of-the-art methods.