BioInfer: a corpus for information extraction in the biomedical domain

Pyysalo, Sampo; Ginter, Filip; Heimonen, Juho; Björne, Jari; Boberg, Jorma; Järvinen, Jouni; Salakoski, Tapio

doi:10.1186/1471-2105-8-50

Cited by 344 publications

(267 citation statements)

References 22 publications

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“…Our method is evaluated on five public corpora: AIMed [13], BioInfer [14], HPRD50 [15], IEPA [16], and LLL [17]. All the corpora, with slightly different annotating policies, are organized in a common format [18] and are generally used in the assessment of PPIe methods.…”

Section: A Experimental Settingsmentioning

confidence: 99%

Improving Kernel-based protein-protein interaction extraction by unsupervised word representation

Guo

Jiang

et al. 2014

2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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As an important branch of biomedical information extraction, Protein-Protein Interaction extraction (PPIe) from biomedical literatures has been widely researched, and machine learning methods have achieved great success for this task. However, the word feature generally adopted in the existing methods suffers badly from vocabulary gap and data sparseness, weakening the classification performance. In this paper, the unsupervised word representation approach is introduced to address these problems. Three word representation methods are adopted to improve the performance of PPIe: distributed representation, vector clustering and Brown clusters representation. Experimental results show that our method outperforms the state-of-the-art methods on five publicly available corpora.

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Section: A Experimental Settingsmentioning

confidence: 99%

Improving Kernel-based protein-protein interaction extraction by unsupervised word representation

Guo

Jiang

et al. 2014

2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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“…It is commonly assumed that the desired information is local to a sentence [7-9,14]. We thus constrain the event extraction at the sentence resolution.…”

Section: Text Preprocessingmentioning

confidence: 99%

“…The former has been used to recognize diverse biological events [8,9]. The latter is designed to model complex events that are part of the gene regulatory processes [10].…”

Section: Identifying Transcriptional Regulation Rate Changing Events mentioning

confidence: 99%

Extracting rate changes in transcriptional regulation from MEDLINE abstracts

Liu

Miao

et al. 2014

BMC Bioinformatics

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BackgroundTime delays are important factors that are often neglected in gene regulatory network (GRN) inference models. Validating time delays from knowledge bases is a challenge since the vast majority of biological databases do not record temporal information of gene regulations. Biological knowledge and facts on gene regulations are typically extracted from bio-literature with specialized methods that depend on the regulation task. In this paper, we mine evidences for time delays related to the transcriptional regulation of yeast from the PubMed abstracts.ResultsSince the vast majority of abstracts lack quantitative time information, we can only collect qualitative evidences of time delays. Specifically, the speed-up or delay in transcriptional regulation rate can provide evidences for time delays (shorter or longer) in GRN. Thus, we focus on deriving events related to rate changes in transcriptional regulation. A corpus of yeast regulation related abstracts was manually labeled with such events. In order to capture these events automatically, we create an ontology of sub-processes that are likely to result in transcription rate changes by combining textual patterns and biological knowledge. We also propose effective feature extraction methods based on the created ontology to identify the direct evidences with specific details of these events. Our ontologies outperform existing state-of-the-art gene regulation ontologies in the automatic rule learning method applied to our corpus. The proposed deterministic ontology rule-based method can achieve comparable performance to the automatic rule learning method based on decision trees. This demonstrates the effectiveness of our ontology in identifying rate-changing events. We also tested the effectiveness of the proposed feature mining methods on detecting direct evidence of events. Experimental results show that the machine learning method on these features achieves an F1-score of 71.43%.ConclusionsThe manually labeled corpus of events relating to rate changes in transcriptional regulation for yeast is available in https://sites.google.com/site/wentingntu/data. The created ontologies summarized both biological causes of rate changes in transcriptional regulation and corresponding positive and negative textual patterns from the corpus. They are demonstrated to be effective in identifying rate-changing events, which shows the benefits of combining textual patterns and biological knowledge on extracting complex biological events.

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“…The biological event extraction refers to the task of detection of typed, text bound events and assignment of proteins as arguments, using basic tools for biological and biomedical text analysis and manually event annotated corpora (GENIA [9], BioInfer [10]). [8].…”

Section: Introductionmentioning

confidence: 99%

A classification approach to extract biological events

Amami¹,

Faiz²,

Elkhlifi³

2013

2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)

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The machine learning methods like support vectors machines, hidden markov model and conditional random fields are the most used methods for implementing natural language processing systems. In this paper, we propose a machine learning approach that can be used for sequential labeling tasks like biological event extraction. Our biological event extraction approach uses Support Vector Machines (SVM) and a composite kernel function to identify triggers and to assign the corresponding arguments. Also, we use a number of features based on both syntactic and contextual information which were automatically learned from the training data.

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BioInfer: a corpus for information extraction in the biomedical domain

Cited by 344 publications

References 22 publications

Improving Kernel-based protein-protein interaction extraction by unsupervised word representation

Improving Kernel-based protein-protein interaction extraction by unsupervised word representation

Extracting rate changes in transcriptional regulation from MEDLINE abstracts

A classification approach to extract biological events

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