Automated extraction of information on protein–protein
  interactions from the biological literature

Ono, Toshihide; Hishigaki, Haretsugu; Tanigami, Akira; Takagi, Toshihisa

doi:10.1093/bioinformatics/17.2.155

Cited by 310 publications

(189 citation statements)

References 8 publications

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“…Our research is also related to the protein-protein interaction (PPI) detection [14], which focuses on discovering protein interactions mentioned in biomedical literature. In medical research, determining protein interaction pairs is crucial to understanding both the functional role of individual proteins and the organization of the entire biological process.…”

Section: Related Workmentioning

confidence: 99%

“…The methods extract text features from sentences to construct learning models, which are then used to detect sentences that mention protein interactions. For instance, Ono et al [14] manually defined a set of syntactic rule-based features covering word and part-ofspeech patterns to represent complex sentences. Xiao et al [18] exploited maximum entropy models to combine diverse lexical, syntactic, and semantic features for PPI extraction.…”

Section: Related Workmentioning

confidence: 99%

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A Composite Kernel Approach for Detecting Interactive Segments in Chinese Topic Documents

Chang

Chen

Hsu

2013

Information Retrieval Technology

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Abstract. Discovering the interactions between persons mentioned in a set of topic documents can help readers construct the background of a topic and facilitate comprehension. In this paper, we propose a rich interactive tree structure to represent syntactic, content, and semantic information in text. We also present a composite kernel classification method that integrates the tree structure with a bigram kernel to identify text segments that mention person interactions in topic documents. Empirical evaluations demonstrate that the proposed tree structure and bigram kernel are effective and the composite kernel approach outperforms well-known relation extraction and PPI methods.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Composite Kernel Approach for Detecting Interactive Segments in Chinese Topic Documents

Chang

Chen

Hsu

2013

Information Retrieval Technology

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“…The SUISEKI system uses regular expressions, with probabilities that reflect the experimental accuracy of each pattern to extract interactions into predefined frame structures (Blaschke & Valencia, 2002). Ono et al manually defined a set of rules based on syntactic features to preprocess complex sentences, with negation structures considered as well (Ono et al, 2001). The BioRAT system uses manually engineered templates that combine lexical and semantic information to identify protein interactions (Corney et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Protein-Protein Interactions Extraction from Biomedical Literatures

Lin¹,

Yang²,

Li³

2011

Biomedical Engineering, Trends, Research and Technologies

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“…Some favour precision by focusing on very specific and well-defined interactions, like protein-protein interactions (e.g. [4][5][6][7][8]), but neglect other biological phenomenons; whereas other stress on recall by extracting general relations (e.g. [9,10]), but face precision issues originating from the important lexical diversity.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Genic Interactions with the Recursive Logical Theory of an Ontology

Manine

Alphonse

Bessières

2010

Computational Linguistics and Intelligent Text Processing

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Abstract. We introduce an Information Extraction (IE) system which uses the logical theory of an ontology as a generalisation of the typical information extraction patterns to extract biological interactions from text. This provides inferences capabilities beyond current approaches: first, our system is able to handle multiple relations; second, it allows to handle dependencies between relations, by deriving new relations from the previously extracted ones, and using inference at a semantic level; third, it addresses recursive or mutually recursive rules. In this context, automatically acquiring the resources of an IE system becomes an ontology learning task: terms, synonyms, conceptual hierarchy, relational hierarchy, and the logical theory of the ontology have to be acquired. We focus on the last point, as learning the logical theory of an ontology, and a fortiori of a recursive one, remains a seldom studied problem. We validate our approach by using a relational learning algorithm, which handles recursion, to learn a recursive logical theory from a text corpus on the bacterium Bacillus subtilis. This theory achieves a good recall and precision for the ten defined semantic relations, reaching a global recall of 67.7% and a precision of 75.5%, but more importantly, it captures complex mutually recursive interactions which were implicitly encoded in the ontology.

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Automated extraction of information on protein–protein interactions from the biological literature

Abstract: The program is available on request from the authors.

Cited by 310 publications

References 8 publications

A Composite Kernel Approach for Detecting Interactive Segments in Chinese Topic Documents

A Composite Kernel Approach for Detecting Interactive Segments in Chinese Topic Documents

Protein-Protein Interactions Extraction from Biomedical Literatures

Extraction of Genic Interactions with the Recursive Logical Theory of an Ontology

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