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https://ceur-ws.org/Vol-761/preface.pdf
Proceedings of the 1st Challenge task on
Drug-Drug Interaction Extraction.
Huelva, Spain, September 7th, 2011.
Edited by:
Isabel Segura-Bedmar, Universidad Carlos III de Madrid, Spain
Paloma Martínez, Universidad Carlos III de Madrid, Spain
Daniel Sánchez Cisneros, Universidad Carlos III de Madrid, Spain
I
II
Welcome
We are pleased to welcome to the DDIExtraction 2011 workshop (First Challenge Task
on Drug-Drug Interaction Extraction) being held in Huelva, Spain on September 7 and
co-located with the 27th Conference of the Spanish Society for Natural Language
Processing, SEPLN 2011. On behalf of the organizing committee, we would like to
thank you for your participation and hope you enjoy the workshop.
The detection of DDI is an important research area in patient safety since these
interactions can become very dangerous and increase health care costs. Although
there are different databases supporting health care professionals in the detection of
DDI, these databases are rarely complete, since their update periods can reach three
years. Drug interactions are frequently reported in journals of clinical pharmacology
and technical reports, making medical literature the most effective source for the
detection of DDI. Thus, the management of DDI is a critical issue due to the
overwhelming amount of information available on them.
Information Extraction (IE) can be of great benefit in the pharmaceutical industry
allowing identification and extraction of relevant information on DDI and providing an
interesting way of reducing the time spent by health care professionals on reviewing
the literature. Moreover, the development of tools for automatically extracting DDI is
essential for improving and updating the drug knowledge databases. Most
investigation has focused on biological relationships (genetic and protein interactions
(PPI)) due mainly to the availability of annotated corpora in the biological domain,
facilitating the evaluation of approaches. Few approaches have focused on the
extraction of DDIs.
The DDIExtraction (Extraction of drug-drug interactions) task focuses on the extraction
of drug-drug interactions from biomedical texts and aims to promote the development
of text mining and information extraction systems applied to the pharmacological
domain in order to reduce time spent by the health care professionals reviewing the
literature for potential drug-drug interactions. Our main goal is to have a benchmark
for the comparison of advanced techniques, rather than competitive aspects.
We would like to thank all the participating teams for submitting their runs and
panelists for presenting their work. We also acknowledge all the members of the
program committee for providing their support in reviewing contributions. Finally, we
would like to thank to Universidad de Huelva, especially the organizers of the SEPLN
2011 conference and all the people that help us to make this workshop possible.
The DDIExtraction 2011 Workshop was partially supported by MA2VICMR consortium
(S2009/TIC-1542) and MULTIMEDICA research project (TIN2010-20644-C03-01).
The DDIExtraction 2011 organizing committee
III
Committees
Organizing Committee
Isabel Segura-Bedmar, Universidad Carlos III de Madrid, Spain
Paloma Martínez, Universidad Carlos III de Madrid, Spain
Daniel Sánchez Cisneros, Universidad Carlos III de Madrid, Spain
Program Committee
Manuel Alcántara, Universidad Autónoma de Madrid, Spain
Manuel de Buenaga, Universidad Europea de Madrid (UEM), Spain
Cesar de Pablo-Sánchez, Innosoft Factory S.L., Spain
Alberto Díaz, Universidad Complutense de Madrid, Spain
Ana García Serrano, Universidad Nacional Educación a Distancia (UNED), Spain
Ana Iglesias, Universidad Carlos III de Madrid, Madrid, Spain
Antonio J. Jimeno Yepes, National Library of Medicine (NLM), Washington DC, USA
Jee-Hyub Kim, EMBL-EBI, UK.
Florian Leitner, Structural Computational Biology Group, CNIO, Spain
Paloma Martínez Fernández, Universidad Carlos III de Madrid, Spain
Jose Luís Martínez Fernández, Universidad Carlos III de Madrid, Spain
Antonio Moreno Sandoval, Universidad Autónoma de Madrid, Spain
Roser Morante, CLiPS - Linguistics Department, University of Antwerp, Belgium
Paolo Rosso, Universidad Politécnica de Valencia, Spain
Isabel Segura-Bedmar, Universidad Carlos III de Madrid, Spain
IV
Table of contents
The 1st DDIExtraction-2011 challenge task: Extraction of Drug-Drug Interactions from
biomedical texts ........................................................................................................................ 1
Isabel Segura-Bedmar, Paloma Martínez, and Daniel Sánchez-Cisneros
Relation Extraction for Drug-Drug Interactions using Ensemble Learning ............................. 11
Philippe Thomas, Mariana Neves, Illes Solt, Domonkos Tikk, and Ulf Leser
Two Different Machine Learning Techniques for Drug-drug Interaction Extraction .............. 19
Md. Faisal Mahbub Chowdhury, Asma Ben Abacha, Alberto Lavelli, and Pierre
Zweigenbau
Drug-drug Interaction Extraction Using Composite Kernels ................................................... 27
Md. Faisal Mahbub Chowdhury, and Alberto Lavelli
Drug-Drug Interaction Extraction with RLS and SVM Classiffers ............................................ 35
Jari Björne, Antti Airola, Tapio Pahikkala, and Tapio Salakoski
Feature selection for Drug-Drug Interaction detection using machine-learning based
approaches .............................................................................................................................. 43
Anne-Lyse Minard, Anne-Laure Ligozat, Brigitte Grau, and Lamia Makour
Automatic Drug-Drug Interaction Detection: A Machine Learning Approach With Maximal
Frequent Sequence Extraction ................................................................................................ 51
Sandra Garcia-Blasco, Santiago M. Mola-Velasco, Roxana Danger, and Paolo
Rosso
A machine learning approach to extract drug–drug interactions in an unbalanced cataset.. 59
Jacinto Mata Vázquez, Ramón Santano, Daniel Blanco, Marcos Lucero, and
Manuel J. Maña López
Drug-Drug Interactions Discovery Based on CRFs SVMs and Rule-Based Methods ............... 67
Stefania Rubrichi, Matteo Gabetta, Riccardo Bellazzi, Cristiana Larizza, and
Silvana Quaglini
An experimental exploration of drug-drug interaction extraction from biomedical texts ..... 75
Man Lan, Jiang Zhao, Kezun Zhang, Honglei Shi, and Jingli Cai
Extraction of drug-drug interactions using all paths graph kernel ......................................... 83
Shreyas Karnik, Abhinita Subhadarshini, Zhiping Wang, Luis Rocha and Lang Li
V
Paper resumen
Relation Extraction for Drug-Drug Interactions using Ensemble
Learning
Two Different Machine Learning Techniques for Drug-drug Interaction
Extraction
Drug-drug Interaction Extraction Using Composite Kernels
Drug-Drug Interaction Extraction with RLS and SVM Classiffers
Feature selection for Drug-Drug Interaction detection using machine-
learning based approaches
Automatic Drug-Drug Interaction Detection: A Machine Learning
Approach With Maximal Frequent Sequence Extraction
A Machine Learning Approach to Extract Drug – Drug Interactions in an
Unbalanced Dataset
Drug-Drug Interactions Discovery Based on CRFs SVMs and Rule-Based
Methods
An experimental exploration of drug-drug interaction extraction from
biomedical texts
Extraction of drug-drug interactions using all paths graph kernel
VI