Potent pairing: ensemble of long short-term memory networks and support vector machine for chemical-protein relation extraction

Autor: Farrokh Mehryary, Jari Björne, Tapio Salakoski, Filip Ginter
Rok vydání: 2018
Předmět:
0301 basic medicine
Support Vector Machine
Source code
Computer science
media_common.quotation_subject
02 engineering and technology
Machine learning
computer.software_genre
General Biochemistry
Genetics and Molecular Biology
Task (project management)
03 medical and health sciences
Deep Learning
Drug Discovery
0202 electrical engineering
electronic engineering
information engineering
Data Mining
Databases
Protein
media_common
ta113
Artificial neural network
business.industry
Proteins
File format
Relationship extraction
Support vector machine
030104 developmental biology
Pharmaceutical Preparations
Path (graph theory)
Original Article
020201 artificial intelligence & image processing
Neural Networks
Computer
Artificial intelligence
General Agricultural and Biological Sciences
business
computer
Databases
Chemical
Sentence
Protein Binding
Information Systems
Zdroj: Database: The Journal of Biological Databases and Curation
ISSN: 1758-0463
Popis: Biomedical researchers regularly discover new interactions between chemical compounds/drugs and genes/proteins, and report them in research literature. Having knowledge about these interactions is crucially important in many research areas such as precision medicine and drug discovery. The BioCreative VI Task 5 (CHEMPROT) challenge promotes the development and evaluation of computer systems that can automatically recognize and extract statements of such interactions from biomedical literature. We participated in this challenge with a Support Vector Machine (SVM) system and a deep learning-based system (ST-ANN), and achieved an F-score of 60.99 for the task. After the shared task, we have significantly improved the performance of the ST-ANN system. Additionally, we have developed a new deep learning-based system (I-ANN) that considerably outperforms the ST-ANN system. Both ST-ANN and I-ANN systems are centered around training an ensemble of artificial neural networks and utilizing different bidirectional Long Short-Term Memory (LSTM) chains for representing the shortest dependency path and/or the full sentence. By combining the predictions of the SVM and the I-ANN systems, we achieved an F-score of 63.10 for the task, improving our previous F-score by 2.11 percentage points. Our systems are fully open-source and publicly available. We highlight that the systems we present in this study are not applicable only to the BioCreative VI Task 5, but can be effortlessly re-trained to extract any types of relations of interest, with no modifications of the source code required, if a manually annotated corpus is provided as training data in a specific file format.
Databáze: OpenAIRE
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