OntoGene web services for biomedical text mining.

Text mining services are rapidly becoming a crucial component of various knowledge management pipelines, for example in the process of database curation, or for exploration and enrichment of biomedical data within the pharmaceutical industry. Traditional architectures, based on monolithic applications, do not offer sufficient flexibility for a wide range of use case scenarios, and therefore open architectures, as provided by web services, are attracting increased interest. We present an approach towards providing advanced text mining capabilities through web services, using a recently proposed standard for textual data interchange (BioC). The web services leverage a state-of-the-art platform for text mining (OntoGene) which has been tested in several community-organized evaluation challenges,with top ranked results in several of them.

BioCreative V track 4: a shared task for the extraction of causal network information using the Biological Expression Language.

Automatic extraction of biological network information is one of the most desired and most complex tasks in biological and medical text mining. Track 4 at BioCreative V attempts to approach this complexity using fragments of large-scale manually curated biological networks, represented in Biological Expression Language (BEL), as training and test data. BEL is an advanced knowledge representation format which has been designed to be both human readable and machine processable. The specific goal of track 4 was to evaluate text mining systems capable of automatically constructing BEL statements from given evidence text, and of retrieving evidence text for given BEL statements. Given the complexity of the task, we designed an evaluation methodology which gives credit to partially correct statements. We identified various levels of information expressed by BEL statements, such as entities, functions, relations, and introduced an evaluation framework which rewards systems capable of delivering useful BEL fragments at each of these levels. The aim of this evaluation method is to help identify the characteristics of the systems which, if combined, would be most useful for achieving the overall goal of automatically constructing causal biological networks from text.

Assisted curation of regulatory interactions and growth conditions of OxyR in E. coli K-12.

Given the current explosion of data within original publications generated in the field of genomics, a recognized bottleneck is the transfer of such knowledge into comprehensive databases. We have for years organized knowledge on transcriptional regulation reported in the original literature of Escherichia coli K-12 into RegulonDB (http://regulondb.ccg.unam.mx), our database that is currently supported by >5000 papers. Here, we report a first step towards the automatic biocuration of growth conditions in this corpus. Using the OntoGene text-mining system (http://www.ontogene.org), we extracted and manually validated regulatory interactions and growth conditions in a new approach based on filters that enable the curator to select informative sentences from preprocessed full papers. Based on a set of 48 papers dealing with oxidative stress by OxyR, we were able to retrieve 100% of the OxyR regulatory interactions present in RegulonDB, including the transcription factors and their effect on target genes. Our strategy was designed to extract, as we did, their growth conditions. This result provides a proof of concept for a more direct and efficient curation process, and enables us to define the strategy of the subsequent steps to be implemented for a semi-automatic curation of original literature dealing with regulation of gene expression in bacteria. This project will enhance the efficiency and quality of the curation of knowledge present in the literature of gene regulation, and contribute to a significant increase in the encoding of the regulatory network of E. coli. RegulonDB Database URL: http://regulondb.ccg.unam.mx OntoGene URL: http://www.ontogene.org.