BioInfer: a corpus for information extraction in the biomedical domain

By Sampo Pyysalo, Filip Ginter, Juho Heimonen, Jari Björne, Jorma Boberg, Jouni Järvinen, Tapio Salakoski. BMC Bioinformatics, 2007

538 citations recorded by the source corpus.

BackgroundLately, there has been a great interest in the application of information extraction methods to the biomedical domain, in particular, to the extraction of relationships of genes, proteins, and RNA from scientific publications. The development and evaluation of such methods requires annotated domain corpora.ResultsWe present BioInfer (Bio Information Extraction Resource), a new public resource providing an annotated corpus of biomedical English. We describe an annotation scheme capturing named entities and their relationships along with a dependency analysis of sentence syntax. We further present ontologies defining the types of entities and relationships annotated in the corpus. Currently, the corpus contains 1100 sentences from abstracts of biomedical research articles annotated for relationships, named entities, as well as syntactic dependencies. Supporting software is provided with the corpus. The corpus is unique in the domain in combining these annotation types for a single set of sentences, and in the level of detail of the relationship annotation.ConclusionWe introduce a corpus targeted at protein, gene, and RNA relationships which serves as a resource for the development of information extraction systems and their components such as parsers and domain analyzers. The corpus will be maintained and further developed with a current version being available at http://www.it.utu.fi/BioInfer.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/cb891d6e79057ac655ed852eef677f1ab07359fb

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Citations in the corpus

Deep learning with word embeddings improves biomedical named entity recognition (2017, methodology): “Both models were created by Pyysalo et al. (2013) using Google’s word2vec (http://bio.”
MAVEN: A Massive General Domain Event Detection Dataset (2020, background): “Some datasets are developed for domain-speciﬁc event extraction on special domains, like the bio-medical domain (Pyysalo et al., 2007; Kim et al., 2008; Thompson et al., 2009; Buyko et al., 2010; N´edellec et al., 2013), literature (Sims et al., 2019), Twitter (Ritter et al., 2012; Guo et al.,…”
All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning (2008, methodology): “Corpora and evaluation criteria We evaluate our method using five publicly available corpora that contain PPI interaction annotation: AImed [4], BioInfer [6], HPRD50 [19], IEPA [20] and LLL [21].”
Frontiers of biomedical text mining: current progress (2007, methodology): “Such annotation requires domain knowledge and significant time: annotation of 1100 sentences in the BioInfer collection reported by Pyysalo et al. [115] was started in 2001.”
CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases (2016, background): “5, precision-recall curves on NYT and BioInfer datasets further show that COTYPE can still achieve descent precision with good recall preserved.”
Construction of an annotated corpus to support biomedical information extraction (2009, background): “, [21] and [23]) are not specifically geared to support the acquisition of semantic frame information for verbs.”
A neural joint model for entity and relation extraction from biomedical text (2017, background): “Recently, various related tasks have been proposed, such as protein-protein interaction detection (PPI) [2], drug-drug interaction detection (DDI) [3], adverse drug event extraction (ADE) [4] and the bacteria biotope task (BB) [5].”
The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes (2008, unknown): “The BioInfer corpus [10], where biological relations are annotated for negation (1100 sentences in size).”
Bridging semantics and syntax with graph algorithms - state-of-the-art of extracting biomedical relations (2017, methodology): “Shared resources for biomedical relation extraction Utility category Data sources Terminology/Ontology/Knowledge Base GO [161], UMLS [58], MeSH [162], Wordnet [118], Verbnet [163], Biothesaurus [122], PDB [101], Uniprot [121], SIDER [164], DrugBank [165], HIVDB [166], RegaDB [167], Entrez Gene [153]”
Concept annotation in the CRAFT corpus (2012, methodology): “We focus primarily on biomedical corpora, as these are obviously most directly related: ABGene [42], BioInfer [43], the CLEF Corpus [44], the FetchProt Corpus [45], the Fourth i2b2/VA Challenge corpus [46], GENETAG [47], GENIA [48,49], GREC [50], the ITI TXM PPI and TE corpora [51], MedPost [52],…”
BioWordVec, improving biomedical word embeddings with subword information and MeSH (2019, methodology): “The public PPI corpora were used for the PPI extraction, including AIMed 29 , BioInfer 30 , IEPA 31 , HPRD50 32 and LLL 33 .”
Multichannel Convolutional Neural Network for Biological Relation Extraction (2016, methodology): “BioInfer [34] was developed by Turku BioNLP group (http://bionlp .”
The Stanford Typed Dependencies Representation (2008, methodology): “They also made available a program and conversion rules that they used to transform Link Grammar relations into SD graphs, which were then hand-corrected (Pyysalo et al., 2007b).”
Corpus annotation for mining biomedical events from literature (2008, methodology): “The annotated corpus consists of 9,372 sentences and 36,114 annotated events, which is by far the largest among similar attempts [53,56].”
A Comprehensive Benchmark of Kernel Methods to Extract Protein–Protein Interactions from Literature (2010, methodology): “In [36], a thorough analysis of five freely available PPI-annotated resources, namely AIMed [18], BioInfer [59], HPRD50 [60], IEPA [61], and LLL [62], was performed.”