The Protein-Protein Interaction tasks of BioCreative III: classification/ranking of articles and linking bio-ontology concepts to full text

Abstract

BackgroundDetermining usefulness of biomedical text mining systems requires realistic task definition and data selection criteria without artificial constraints, measuring performance aspects that go beyond traditional metrics. The BioCreative III Protein-Protein Interaction (PPI) tasks were motivated by such considerations, trying to address aspects including how the end user would oversee the generated output, for instance by providing ranked results, textual evidence for human interpretation or measuring time savings by using automated systems. Detecting articles describing complex biological events like PPIs was addressed in the Article Classification Task (ACT), where participants were asked to implement tools for detecting PPI-describing abstracts. Therefore the BCIII-ACT corpus was provided, which includes a training, development and test set of over 12,000 PPI relevant and non-relevant PubMed abstracts labeled manually by domain experts and recording also the human classification times. The Interaction Method Task (IMT) went beyond abstracts and required mining for associations between more than 3,500 full text articles and interaction detection method ontology concepts that had been applied to detect the PPIs reported in them.ResultsA total of 11 teams participated in at least one of the two PPI tasks (10 in ACT and 8 in the IMT) and a total of 62 persons were involved either as participants or in preparing data sets/evaluating these tasks. Per task, each team was allowed to submit five runs offline and another five online via the BioCreative Meta-Server. From the 52 runs submitted for the ACT, the highest Matthew's Correlation Coefficient (MCC) score measured was 0.55 at an accuracy of 89% and the best AUC iP/R was 68%. Most ACT teams explored machine learning methods, some of them also used lexical resources like MeSH terms, PSI-MI concepts or particular lists of verbs and nouns, some integrated NER approaches. For the IMT, a total of 42 runs were evaluated by comparing systems against manually generated annotations done by curators from the BioGRID and MINT databases. The highest AUC iP/R achieved by any run was 53%, the best MCC score 0.55. In case of competitive systems with an acceptable recall (above 35%) the macro-averaged precision ranged between 50% and 80%, with a maximum F-Score of 55%.ConclusionsThe results of the ACT task of BioCreative III indicate that classification of large unbalanced article collections reflecting the real class imbalance is still challenging. Nevertheless, text-mining tools that report ranked lists of relevant articles for manual selection can potentially reduce the time needed to identify half of the relevant articles to less than 1/4 of the time when compared to unranked results. Detecting associations between full text articles and interaction detection method PSI-MI terms (IMT) is more difficult than might be anticipated. This is due to the variability of method term mentions, errors resulting from pre-processing of articles provided as PDF files, and the heteroge...

BioNLP 2017

“…As the biomedical literature continues to grow at an exponential rate (Lu, 2011), automated tools, such as text mining, are necessary to enable extracting information from the literature in a timely and efficient manner. Text mining is a means to automatically extract information from the literature without requiring manual curation of a large number of documents.…”

“…PIE 1 the search is a web service that provides an alternate way of querying PubMed for biologists and database curators. The returned articles are ranked based on their probability of describing protein-protein interactions, using a very competitive algorithm and the winner of BioCreative III ACT competition (Krallinger, Vazquez et al 2011). tmVar 2 is another text mining tool that is the current gold-standard for recognizing sequence variants in PubMed literature.…”

Towards a Protein–Protein Interaction information extraction system: Recognizing named entities

“…Eight teams participated in this task [10]. Six of them used ML approaches to perform the required task.…”

“…This is done for two main reasons. On the one hand, some authors have recognized a few clues that link section names and words with specific detection methods (various examples can be found in [10]). On the other, hand our final system should be able to provide a friendly interface for obtaining feedback from users, in which the article structure is used to make visualization and user interaction easier.…”

“…To assess the performance of this module is a difficult task, as there are no available corpora in the PPI context tagged with all these entities. We have, however, used this module to solve the IMT task of BIOCREATIVE III [10], which consists in the recognition of the interaction detection methods used in PPI discovery.…”