Our work focuses on the extraction of semantic relations between specific biological entities (such as genes, proteins, drugs, diseases) from the biomedical scientific literature (e.g. PubMed). Our approach is based upon high-precision robust syntactic parsing of the target documents, combined with advanced machine learning techniques. The selected publications (below in this page) provide a good overview of the techniques used in the OntoGene system.

We consider community-run evaluation challenges as the best way to provide an independent and unbiased evaluation of text mining tools. We participated in the BioCreative challenges (since 2006), the BioNLP shared task (2009) and CALBC (2010). In BioCreative II  (2006) we obtained very good results in the tasks of detecting protein interactions, and the best results in detecting experimental methods [2]. In BioCreative II.5 (2009) we obtained the best results in the PPI task (extraction of protein-protein interactions) [4]. In BioCreative III (2010) we were among the three best ranked teams in the PPI-ACT task (binary decision whether a paper contains a curatable protein-protein interaction) and PPI-IMT task (detection of experimental methods). In the latter task, we were the only group which managed to return results with full recall (while maintaining near-best AUC score). Our ODIN tool received favourable comments from curators in the IAT (interactive) task. In the "triage task" of BioCreative 2012 we obtained once again the best overall results.

NEWS!

    February 2013: The paper describing our participation in the 'Triage' task of BioCreative 2012 has been published (we obtained the best overall results).

    September 2012: The 5th International Symposium on Semantic Mining in Biomedicine (SMBM), which we organized at our premises, was a very successful event. 

    July 2012: Two additional journal papers accepted for publication (see [7] and [8] below). OntoGene continues a very successful year!

    April 2012: The long awaited paper which describes the text mining technologies used for our pharmacogenomics experiments has finally been accepted for publication [6]:   doi:10.1016/j.jbi.2012.04.014.

    March 2012: Our work on assisted curation, in collaboration with the PharmGKB group at Stanford University has now reached the stage of journal publication [5]:  doi:10.1093/database/bas021.

    February 2012: We are organizing the next edition of the "International Symposium on Semantic Mining in Medicine" (SMBM 2012). 

HIGHLIGHTS

• Our participation in the "triage task" of BioCreative 2012 was rewarded with the best overall results, in particular due to very accurate entity recognition. 
• We are currently funded by the Swiss National Science Foundation through the SASEBio project (Semi-Automated Semantic Enrichment of the Biomedical Literature). Duration: 3 years. Funding: 3 post-docs. Additional financial and practical support provided by Novartis Pharma AG, NIBR-IT, Text Mining Services, CH-4002, Basel, Switzerland. 
• Additionally a post-doc in our group (Gintarė Grigonytė) is funded by a Sciex fellowship (see BioTermEvo project).
• In the BioCreative III shared task (2010) we were one of only two groups which participated in all of the tasks, obtaining satisfactory results in all of them. We were involved as co-authors in four journal papers in the special issue of BMC Bioinformatics on BioCreative III.
• In the CALBC competition (2009/2010), which targets large-scale entity detection across the biomedical literature, our system achieved best results in the 'species' and 'diseases' categories.
• In 2008/2009 we have been funded by the  SNF project Detection of Biological Interactions from Biomedical Literature (grant 100014-118396/1).  
• Our participation in the BioCreative II.5 competitive evaluation (2009) of biomedical text mining systems resulted in the best run for the detection of protein-protein interactions (according to the 'raw' AUC iP/R metric). Our system was overall considered as one of the best three.