Mining of relations between proteins over biomedical scientific literature using a deep-linguistic approach

Rinaldi, Fabio; Schneider, Gerold; Kaljurand, Kaarel; Heß, Michael; Andronis, Christos; Konstandi, Ourania; Persidis, Aris

doi:10.1016/j.artmed.2006.08.005

Cited by 64 publications

(40 citation statements)

References 9 publications

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“…Also, they actively utilize dependency parsing information to detect the edges. This is because several previous approaches have already improved their performance by using features extracted from dependency parsing information [4,5,[10][11][12]. Furthermore, the distance between an event trigger and its arguments tends to be much shorter in the dependency path than in the sentence [8].…”

Section: Previous Workmentioning

confidence: 99%

“…To analyze biomedical literature, some previous approaches have focused only on recognizing named entities (such as proteins), while some recent approaches have emphasized the problem of identifying the interaction between two entities [1][2][3][4][5][6]. They are interested in extracting binary relations, such as protein-protein interactions and disease-gene associations.…”

Section: Introductionmentioning

confidence: 99%

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A Maximum Entropy-Based Bio-Molecular Event Extraction Model that Considers Event Generation

Lee¹,

Park

Rim³

et al. 2014

J Inf Process Syst

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In this paper, we propose a maximum entropy-based model, which can mathematically explain the biomolecular event extraction problem. The proposed model generates an event table, which can represent the relationship between an event trigger and its arguments. The complex sentences with distinctive event structures can be also represented by the event table. Previous approaches intuitively designed a pipeline system, which sequentially performs trigger detection and arguments recognition, and thus, did not clearly explain the relationship between identified triggers and arguments. On the other hand, the proposed model generates an event table that can represent triggers, their arguments, and their relationships. The desired events can be easily extracted from the event table. Experimental results show that the proposed model can cover 91.36% of events in the training dataset and that it can achieve a 50.44% recall in the test dataset by using the event table.

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Section: Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Maximum Entropy-Based Bio-Molecular Event Extraction Model that Considers Event Generation

Lee¹,

Park

Rim³

et al. 2014

J Inf Process Syst

View full text Add to dashboard Cite

show abstract

“…생물학 데이터를 대 상으로 가장 많이 추출되는 정보인 단백질 상호작용을 인식 하기 위해, 기존 연구들은 전체 구문분석기로부터 도출된 구문 정보를 이용함으로써 향상된 성능을 보였다 [6,7] …”

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Detection of Protein Subcellular Localization based on Syntactic Dependency Paths

Kim¹

2008

The KIPS Transactions:PartB

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A protein's subcellular localization is considered an essential part of the description of its associated biomolecular phenomena. As the volume of biomolecular reports has increased, there has been a great deal of research on text mining to detect protein subcellular localization information in documents. It has been argued that linguistic information, especially syntactic information, is useful for identifying the subcellular localizations of proteins of interest. However, previous systems for detecting protein subcellular localization information used only shallow syntactic parsers, and showed poor performance. Thus, there remains a need to use a full syntactic parser and to apply deep linguistic knowledge to the analysis of text for protein subcellular localization information. In addition, we have attempted to use semantic information from the WordNet thesaurus.To improve performance in detecting protein subcellular localization information, this paper proposes a three-step method based on a full syntactic dependency parser and WordNet thesaurus. In the first step, we constructed syntactic dependency paths from each protein to its location candidate, and then converted the syntactic dependency paths into dependency trees. In the second step, we retrieved root information of the syntactic dependency trees. In the final step, we extracted syn-semantic patterns of protein subtrees and location subtrees. From the root and subtree nodes, we extracted syntactic category and syntactic direction as syntactic information, and synset offset of the WordNet thesaurus as semantic information. According to the root information and syn-semantic patterns of subtrees from the training data, we extracted (protein, localization) pairs from the test sentences. Even with no biomolecular knowledge, our method showed reasonable performance in experimental results using Medline abstract data. Our proposed method gave an F-measure of 74.53% for training data and 58.90% for test data, significantly outperforming previous methods, by 12-25%.

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“…The parser is very fast and robust, it parses the entire BNC in little over 24 hours. It has been applied in many areas of research, for example information retrieval (Bayer et al 2004), relation mining in Biomedicine (Rinaldi et al 2007) and psycholinguistics (Schneider 2005). It has been developed by one of the authors and is described in detail in Schneider (2007) 4 .…”

mentioning

confidence: 99%

Parser-based analysis of syntax-lexis interactions

Lehmann

Schneider

2009

Corpora: Pragmatics and Discourse

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Mining of relations between proteins over biomedical scientific literature using a deep-linguistic approach

Cited by 64 publications

References 9 publications

A Maximum Entropy-Based Bio-Molecular Event Extraction Model that Considers Event Generation

A Maximum Entropy-Based Bio-Molecular Event Extraction Model that Considers Event Generation

Detection of Protein Subcellular Localization based on Syntactic Dependency Paths

Parser-based analysis of syntax-lexis interactions

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