BackgroundCompetitions in text mining have been used to measure the performance of automatic text processing solutions against a manually annotated gold standard corpus (GSC). The preparation of the GSC is time-consuming and costly and the final corpus consists at the most of a few thousand documents annotated with a limited set of semantic groups. To overcome these shortcomings, the CALBC project partners (PPs) have produced a large-scale annotated biomedical corpus with four different semantic groups through the harmonisation of annotations from automatic text mining solutions, the first version of the Silver Standard Corpus (SSC-I). The four semantic groups are chemical entities and drugs (CHED), genes and proteins (PRGE), diseases and disorders (DISO) and species (SPE). This corpus has been used for the First CALBC Challenge asking the participants to annotate the corpus with their text processing solutions.ResultsAll four PPs from the CALBC project and in addition, 12 challenge participants (CPs) contributed annotated data sets for an evaluation against the SSC-I. CPs could ignore the training data and deliver the annotations from their genuine annotation system, or could train a machine-learning approach on the provided pre-annotated data. In general, the performances of the annotation solutions were lower for entities from the categories CHED and PRGE in comparison to the identification of entities categorized as DISO and SPE. The best performance over all semantic groups were achieved from two annotation solutions that have been trained on the SSC-I.The data sets from participants were used to generate the harmonised Silver Standard Corpus II (SSC-II), if the participant did not make use of the annotated data set from the SSC-I for training purposes. The performances of the participants’ solutions were again measured against the SSC-II. The performances of the annotation solutions showed again better results for DISO and SPE in comparison to CHED and PRGE.ConclusionsThe SSC-I delivers a large set of annotations (1,121,705) for a large number of documents (100,000 Medline abstracts). The annotations cover four different semantic groups and are sufficiently homogeneous to be reproduced with a trained classifier leading to an average F-measure of 85%. Benchmarking the annotation solutions against the SSC-II leads to better performance for the CPs’ annotation solutions in comparison to the SSC-I.

BackgroundMutation impact extraction is a hitherto unaccomplished task in state of the art mutation extraction systems. Protein mutations and their impacts on protein properties are hidden in scientific literature, making them poorly accessible for protein engineers and inaccessible for phenotype-prediction systems that currently depend on manually curated genomic variation databases.ResultsWe present the first rule-based approach for the extraction of mutation impacts on protein properties, categorizing their directionality as positive, negative or neutral. Furthermore protein and mutation mentions are grounded to their respective UniProtKB IDs and selected protein properties, namely protein functions to concepts found in the Gene Ontology. The extracted entities are populated to an OWL-DL Mutation Impact ontology facilitating complex querying for mutation impacts using SPARQL. We illustrate retrieval of proteins and mutant sequences for a given direction of impact on specific protein properties. Moreover we provide programmatic access to the data through semantic web services using the SADI (Semantic Automated Discovery and Integration) framework.ConclusionWe address the problem of access to legacy mutation data in unstructured form through the creation of novel mutation impact extraction methods which are evaluated on a corpus of full-text articles on haloalkane dehalogenases, tagged by domain experts. Our approaches show state of the art levels of precision and recall for Mutation Grounding and respectable level of precision but lower recall for the task of Mutant-Impact relation extraction. The system is deployed using text mining and semantic web technologies with the goal of publishing to a broad spectrum of consumers.

BackgroundMutation impact extraction is an important task designed to harvest relevant annotations from scientific documents for reuse in multiple contexts. Our previous work on text mining for mutation impacts resulted in (i) the development of a GATE-based pipeline that mines texts for information about impacts of mutations on proteins, (ii) the population of this information into our OWL DL mutation impact ontology, and (iii) establishing an experimental semantic database for storing the results of text mining.ResultsThis article explores the possibility of using the SADI framework as a medium for publishing our mutation impact software and data. SADI is a set of conventions for creating web services with semantic descriptions that facilitate automatic discovery and orchestration. We describe a case study exploring and demonstrating the utility of the SADI approach in our context. We describe several SADI services we created based on our text mining API and data, and demonstrate how they can be used in a number of biologically meaningful scenarios through a SPARQL interface (SHARE) to SADI services. In all cases we pay special attention to the integration of mutation impact services with external SADI services providing information about related biological entities, such as proteins, pathways, and drugs.ConclusionWe have identified that SADI provides an effective way of exposing our mutation impact data such that it can be leveraged by a variety of stakeholders in multiple use cases. The solutions we provide for our use cases can serve as examples to potential SADI adopters trying to solve similar integration problems.

Epidemiology and experimental studies provide an overwhelming support of the notion that diets high in red or processed meat accompany an elevated risk of developing pre-neoplastic colorectal adenoma and frank colorectal carcinoma (CRC). The underlying mechanisms are disputed; thus several hypotheses have been proposed. A large body of reports converges, however, on haem and nitrosyl haem as major contributors to the CRC development, presumably acting through various mechanisms. Apart from a potentially higher intestinal mutagenic load among consumers on a diet rich in red/processed meat, other mechanisms involving subtle interference with colorectal stem/progenitor cell survival or maturation are likewise at play. From an overarching perspective, suggested candidate mechanisms for red/processed meat-induced CRC appear as three partly overlapping tenets: (i) increased N-nitrosation/oxidative load leading to DNA adducts and lipid peroxidation in the intestinal epithelium, (ii) proliferative stimulation of the epithelium through haem or food-derived metabolites that either act directly or subsequent to conversion, and (iii) higher inflammatory response, which may trigger a wide cascade of pro-malignant processes. In this review, we summarize and discuss major findings of the area in the context of potentially pertinent mechanisms underlying the above-mentioned association between consumption of red/processed meat and increased risk of developing CRC.

Structurally different chemical substances may cause similar systemic effects in mammalian cells. It is therefore necessary to go beyond structural comparisons to quantify similarity in terms of their bioactivities. In this work, we introduce a generic methodology to achieve this on the basis of Network Biology principles and using publicly available molecular network topology information. An implementation of this method, denoted QuantMap, is outlined and applied to antidiabetic drugs, NSAIDs, 17β-estradiol, and 12 substances known to disrupt estrogenic pathways. The similarity of any pair of compounds is derived from topological comparison of intracellular protein networks, directly and indirectly associated with the respective query chemicals, via a straightforward pairwise comparison of ranked proteins. Although output derived from straightforward chemical/structural similarity analysis provided some guidance on bioactivity, QuantMap produced substance interrelationships that align well with reports on their respective perturbation properties. We believe that QuantMap has potential to provide substantial assistance to drug repositioning, pharmacology evaluation, and toxicology risk assessment.