Introduction: MicroRNAs (miRNAs) have demonstrated their potential as post-transcriptional gene expression regulators, participating in a wide spectrum of regulatory events such as apoptosis, differentiation, and stress response. Apart from the role of miRNAs in normal physiology, their dysregulation is implicated in a vast array of diseases. Dissection of miRNA-related associations are valuable for contemplating their mechanism in diseases, leading to the discovery of novel miRNAs for disease prognosis, diagnosis, and therapy. Motivation: Apart from databases and prediction tools, miRNA-related information is largely available as unstructured text. Manual retrieval of these associations can be labor-intensive due to steadily growing number of publications. Additionally, most of the published miRNA entity recognition methods are keyword based, further subjected to manual inspection for retrieval of relations. Despite the fact that several databases host miRNA-associations derived from text, lower sensitivity and lack of published details for miRNA entity recognition and associated relations identification has motivated the need for developing comprehensive methods that are freely available for the scientific community. Additionally, the lack of a standard corpus for miRNA-relations has caused difficulty in evaluating the available systems. We propose methods to automatically extract mentions of miRNAs, species, genes/proteins, disease, and relations from scientific literature. Our generated corpora, along with dictionaries, and miRNA regular expression are freely available for academic purposes. To our knowledge, these resources are the most comprehensive developed so far. Results: The identification of specific miRNA mentions reaches a recall of 0.94 and precision of 0.93. Extraction of miRNA-disease and miRNA-gene relations lead to an F1 score of up to 0.76. A comparison of the information extracted by our approach to the databases miR2Disease and miRSel for the extraction of Alzheimer's disease related relations shows the capability of our proposed methods in identifying correct relations with improved sensitivity. The published resources and described methods can help the researchers for maximal retrieval of miRNA-relations and generation of miRNA-regulatory networks. Availability: The training and test corpora, annotation guidelines, developed dictionaries, and supplementary files are available at http://www.scai.fraunhofer.de/mirna-corpora.html

Introduction: MicroRNAs (miRNAs) have demonstrated their potential as post-transcriptional gene expression regulators, participating in a wide spectrum of regulatory events such as apoptosis, differentiation, and stress response. Apart from the role of miRNAs in normal physiology, their dysregulation is implicated in a vast array of diseases. Dissection of miRNA-related associations are valuable for contemplating their mechanism in diseases, leading to the discovery of novel miRNAs for disease prognosis, diagnosis, and therapy. Motivation: Apart from databases and prediction tools, miRNA-related information is largely available as unstructured text. Manual retrieval of these associations can be labor-intensive due to steadily growing number of publications. Additionally, most of the published miRNA entity recognition methods are keyword based, further subjected to manual inspection for retrieval of relations. Despite the fact that several databases host miRNA-associations derived from text, lower sensitivity and lack of published details for miRNA entity recognition and associated relations identification has motivated the need for developing comprehensive methods that are freely available for the scientific community. Additionally, the lack of a standard corpus for miRNA-relations has caused difficulty in evaluating the available systems. We propose methods to automatically extract mentions of miRNAs, species, genes/proteins, disease, and relations from scientific literature. Our generated corpora, along with dictionaries, and miRNA regular expression are freely available for academic purposes. To our knowledge, these resources are the most comprehensive developed so far. Results: The identification of specific miRNA mentions reaches a recall of 0.94 and precision of 0.93. Extraction of miRNA-disease and miRNA-gene relations lead to an F1 score of up to 0.76. A comparison of the information extracted by our approach to the databases miR2Disease and miRSel for the extraction of Alzheimer's disease related relations shows the capability of our proposed methods in identifying correct relations with improved sensitivity. The published resources and described methods can help the researchers for maximal retrieval of miRNA-relations and generation of miRNA-regulatory networks. Availability: The training and test corpora, annotation guidelines, developed dictionaries, and supplementary files are available at http://www.scai.fraunhofer.de/mirna-corpora.html

Introduction: MicroRNAs (miRNAs) have demonstrated their potential as post-transcriptional gene expression regulators, participating in a wide spectrum of regulatory events such as apoptosis, differentiation, and stress response. Apart from the role of miRNAs in normal physiology, their dysregulation is implicated in a vast array of diseases. Dissection of miRNA-related associations are valuable for contemplating their mechanism in diseases, leading to the discovery of novel miRNAs for disease prognosis, diagnosis, and therapy.Motivation: Apart from databases and prediction tools, miRNA-related information is largely available as unstructured text. Manual retrieval of these associations can be labor-intensive due to steadily growing number of publications. Additionally, most of the published miRNA entity recognition methods are keyword based, further subjected to manual inspection for retrieval of relations. Despite the fact that several databases host miRNA-associations derived from text, lower sensitivity and lack of published details for miRNA entity recognition and associated relations identification has motivated the need for developing comprehensive methods that are freely available for the scientific community. Additionally, the lack of a standard corpus for miRNA-relations has caused difficulty in evaluating the available systems.We propose methods to automatically extract mentions of miRNAs, species, genes/proteins, disease, and relations from scientific literature. Our generated corpora, along with dictionaries, and miRNA regular expression are freely available for academic purposes. To our knowledge, these resources are the most comprehensive developed so far.Results: The identification of specific miRNA mentions reaches a recall of 0.94 and precision of 0.93. Extraction of miRNA-disease and miRNA-gene relations lead to an F1 score of up to 0.76. A comparison of the information extracted by our approach to the databases miR2Disease and miRSel for the extraction of Alzheimer's disease related relations shows the capability of our proposed methods in identifying correct relations with improved sensitivity. The published resources and described methods can help the researchers for maximal retrieval of miRNA-relations and generation of miRNA-regulatory networks.Availability: The training and test corpora, annotation guidelines, developed dictionaries, and supplementary files are available at http://www.scai.fraunhofer.de/mirna-corpora.html

Automatic relation extraction provides great support for scientists and database curators in dealing with the extensive amount of biomedical textual data. The DDIExtraction 2013 challenge poses the task of detecting drugdrug interactions and further categorizing them into one of the four relation classes. We present our machine learning system which utilizes lexical, syntactical and semantic based feature sets. Resampling, balancing and ensemble learning experiments are performed to infer the best configuration. For general drugdrug relation extraction, the system achieves 70.4% in F1 score.

Manual annotation is a tedious and time consuming process, usually needed for generating training corpora to be used in a machine learning scenario.The distant supervision paradigm aims at automatically generating such corpora from structured data.The active learning paradigm aims at reducing the effort needed for manual annotation.We explore active and distant learning approaches jointly to limit the amount of automatically generated data needed for the use case of relation extraction by increasing the quality of the annotations.The main idea of using distantly labeled corpora is that they can simplify and speed-up the generation of models, e. g. for extracting relationships between entities of interest, while the selection of instances is typically performed randomly.We propose the use of query-by-committee to select instances instead.This approach is similar to the active learning paradigm, with a difference that unlabeled instances are weakly annotated, rather than by human experts.Different strategies using low or high confidence are compared to random selection.Experiments on publicly available data sets for detection of protein-protein interactions show a statistically significant improvement in F1 measure when adding instances with a high agreement of the committee.

Interpreting the rapidly increasing amount of experimental data requires the availability and representation of biological knowledge in a computable form. The Biological expression language (BEL) encodes the data in form of causal relationships, which describe the association between biological events. BEL can successfully be applied to large data and support causal reasoning and hypothesis generation. With the rapid growth of biomedical literature, automated methods are a crucial prerequisite for handling and encoding the available knowledge. The BioNLP shared tasks support the development of such tools and provide a linguistically motivated format for the annotation of relations. On the other hand, BEL statements and the corresponding evidence sentences might be a valuable resource for future BioNLP shared task training data generation. In this paper, we briefly introduce BEL and investigate how far BioNLP-shared task annotations could be converted to BEL statements and in such a way directly support BEL statement generation. We present the first results of the automatic BEL statement generation and emphasize the need for more training data that captures the underlying biological meaning.

Relation extraction is frequently and successfully addressed by machine learning methods. The downside of this approach is the need for annotated training data, typically generated in tedious manual, cost intensive work. Distantly supervised approaches make use of weakly annotated data, which can be derived automatically. Recent work in the biomedical domain has applied distant supervision for protein-protein interaction (PPI) with reasonable results, by employing the IntAct database. Training from distantly labeled corpora is more challenging than from manually curated ones, as such data is inherently noisy. With this paper, we make two corpora publicly available to the community to allow for comparison of different methods that deal with the noise in a uniform setting. The first corpus is addressing protein-protein interaction (PPI), based on named entity recognition and the use of IntAct and KUPS databases, the second is concerned with drug-drug interaction (DDI), making use of the database DrugBank. Both corpora are in addition labeled with 5 state-of-the-art classifiers trained on annotated data, to allow for development of filter methods. Furthermore, we present in short our approach and results for distant supervision on these corpora as a strong baseline for future research.

Relation extraction is frequently and successfully addressed by machine learning methods. The downside of this approach is the need for annotated training data, typically generated in tedious manual, cost intensive work. Distantly supervised approaches make use of weakly annotated data, like automatically annotated corpora. Recent work in the biomedical domain has applied distant supervision for protein-protein interaction (PPI) with reasonable results making use of the IntAct database. Such data is typically noisy and heuristics to filter the data are commonly applied. We propose a constraint to increase the quality of data used for training based on the assumption that no self-interaction of real-world objects are described in sentences. In addition, we make use of the University of Kansas Proteomics Service (KUPS) database. These two steps show an increase of 7 percentage points (pp) for the PPI corpus AIMed. We demonstrate the broad applicability of our approach by using the same workflow for the analysis of drug-drug interactions, utilizing relationships available from the drug database DrugBank. We achieve 37.31% in F1 measure without manually annotated training data on an independent test set.