9,731 research outputs found
Ranking Interactions for a Curation Task
One of the key pieces of information which biomedical text mining systems are expected to extract from the literature are interactions among different types of biomedical entities (proteins, genes, diseases, drugs, etc.). Different types of entities might be considered, for example protein-protein interactions have been extensively studied as part of the Bio Creative competitive evaluations. However, more complex interactions such as those among genes, drugs, and diseases are increasingly of interest. Different databases have been used as reference for the evaluation of extraction and ranking techniques. The aim of this paper is to describe a machine-learning based reranking approach for candidate interactions extracted from the literature. The results are evaluated using data derived from the Pharm GKB database. The importance of a good ranking is particularly evident when the results are applied to support human curators
The potential of text mining in data integration and network biology for plant research : a case study on Arabidopsis
Despite the availability of various data repositories for plant research, a wealth of information currently remains hidden within the biomolecular literature. Text mining provides the necessary means to retrieve these data through automated processing of texts. However, only recently has advanced text mining methodology been implemented with sufficient computational power to process texts at a large scale. In this study, we assess the potential of large-scale text mining for plant biology research in general and for network biology in particular using a state-of-the-art text mining system applied to all PubMed abstracts and PubMed Central full texts. We present extensive evaluation of the textual data for Arabidopsis thaliana, assessing the overall accuracy of this new resource for usage in plant network analyses. Furthermore, we combine text mining information with both protein-protein and regulatory interactions from experimental databases. Clusters of tightly connected genes are delineated from the resulting network, illustrating how such an integrative approach is essential to grasp the current knowledge available for Arabidopsis and to uncover gene information through guilt by association. All large-scale data sets, as well as the manually curated textual data, are made publicly available, hereby stimulating the application of text mining data in future plant biology studies
New Resources and Perspectives for Biomedical Event Extraction
Event extraction is a major focus of recent work in biomedical information extraction. Despite substantial advances, many challenges still remain for reliable automatic extraction of events from text. We introduce a new biomedical event extraction resource consisting of analyses automatically created by systems participating in the recent BioNLP Shared Task (ST) 2011. In providing for the first time the outputs of a broad set of state-ofthe-art event extraction systems, this resource opens many new opportunities for studying aspects of event extraction, from the identification of common errors to the study of effective approaches to combining the strengths of systems. We demonstrate these opportunities through a multi-system analysis on three BioNLP ST 2011 main tasks, focusing on events that none of the systems can successfully extract. We further argue for new perspectives to the performance evaluation of domain event extraction systems, considering a document-level, âoff-the-page â representation and evaluation to complement the mentionlevel evaluations pursued in most recent work.
A Linear Classifier Based on Entity Recognition Tools and a Statistical Approach to Method Extraction in the Protein-Protein Interaction Literature
We participated, in the Article Classification and the Interaction Method
subtasks (ACT and IMT, respectively) of the Protein-Protein Interaction task of
the BioCreative III Challenge. For the ACT, we pursued an extensive testing of
available Named Entity Recognition and dictionary tools, and used the most
promising ones to extend our Variable Trigonometric Threshold linear
classifier. For the IMT, we experimented with a primarily statistical approach,
as opposed to employing a deeper natural language processing strategy. Finally,
we also studied the benefits of integrating the method extraction approach that
we have used for the IMT into the ACT pipeline. For the ACT, our linear article
classifier leads to a ranking and classification performance significantly
higher than all the reported submissions. For the IMT, our results are
comparable to those of other systems, which took very different approaches. For
the ACT, we show that the use of named entity recognition tools leads to a
substantial improvement in the ranking and classification of articles relevant
to protein-protein interaction. Thus, we show that our substantially expanded
linear classifier is a very competitive classifier in this domain. Moreover,
this classifier produces interpretable surfaces that can be understood as
"rules" for human understanding of the classification. In terms of the IMT
task, in contrast to other participants, our approach focused on identifying
sentences that are likely to bear evidence for the application of a PPI
detection method, rather than on classifying a document as relevant to a
method. As BioCreative III did not perform an evaluation of the evidence
provided by the system, we have conducted a separate assessment; the evaluators
agree that our tool is indeed effective in detecting relevant evidence for PPI
detection methods.Comment: BMC Bioinformatics. In Pres
Aggregating Content and Network Information to Curate Twitter User Lists
Twitter introduced user lists in late 2009, allowing users to be grouped
according to meaningful topics or themes. Lists have since been adopted by
media outlets as a means of organising content around news stories. Thus the
curation of these lists is important - they should contain the key information
gatekeepers and present a balanced perspective on a story. Here we address this
list curation process from a recommender systems perspective. We propose a
variety of criteria for generating user list recommendations, based on content
analysis, network analysis, and the "crowdsourcing" of existing user lists. We
demonstrate that these types of criteria are often only successful for datasets
with certain characteristics. To resolve this issue, we propose the aggregation
of these different "views" of a news story on Twitter to produce more accurate
user recommendations to support the curation process
- âŚ