Sequential pattern mining for discovering gene interactions and their contextual information from biomedical texts

Journal of Biomedical Semantics

Jean-Luc Manguin⁷  Jiří Kléma¹  Olivier Gandrillon⁶  Bruno Crémilleux⁷  Christophe Rigotti⁴  Marc Plantevit³  Thierry Charnois⁵  Peggy Cellier² 

[1] Faculty of Electrical Engineering, Czech Technical University, Prague, Czech Republic;INSA de Rennes, IRISA, UMR6074, Rennes F-35042, France;Université Lyon 1, LIRIS, UMR5205, Lyon F-69622, France;INSA de Lyon, LIRIS, UMR5205, Lyon F-69621, France;Université de Paris 13, LIPN, UMR7030, Villetaneuse F-93430, France;Université Lyon 1, CGMC, UMR5534, Lyon F-69622, France;Université de Caen, GREYC, UMR6072, Caen F-14032, France

关键词: Gene interactions;    Information extraction;    Natural language processing;    Sequential pattern mining;    Data mining;   

Others  :  1209188
DOI  :  10.1186/s13326-015-0023-3

 received in 2013-07-25, accepted in 2015-04-22,  发布年份 2015

【 摘 要 】

Background

Discovering gene interactions and their characterizations from biological text collections is a crucial issue in bioinformatics. Indeed, text collections are large and it is very difficult for biologists to fully take benefit from this amount of knowledge. Natural Language Processing (NLP) methods have been applied to extract background knowledge from biomedical texts. Some of existing NLP approaches are based on handcrafted rules and thus are time consuming and often devoted to a specific corpus. Machine learning based NLP methods, give good results but generate outcomes that are not really understandable by a user.

Results

We take advantage of an hybridization of data mining and natural language processing to propose an original symbolic method to automatically produce patterns conveying gene interactions and their characterizations. Therefore, our method not only allows gene interactions but also semantics information on the extracted interactions (e.g., modalities, biological contexts, interaction types) to be detected. Only limited resource is required: the text collection that is used as a training corpus. Our approach gives results comparable to the results given by state-of-the-art methods and is even better for the gene interaction detection in AIMed.

Conclusions

Experiments show how our approach enables to discover interactions and their characterizations. To the best of our knowledge, there is few methods that automatically extract the interactions and also associated semantics information. The extracted gene interactions from PubMed are available through a simple web interface at https://bingotexte.greyc.fr/ webcite. The software is available at https://bingo2.greyc.fr/?q=node/22 webcite.

【 授权许可】

   
2015 Cellier et al.; licensee BioMed Central.

【 图 表 】

Figure 1.

Figure 2.

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