Extracting Protein-Protein Interaction from Biomedical Text Using Additional Shallow Parsing Information

Yu, Huanhuan; Qian, Longhua; Zhou, Guodong; Zhu, Qiaoming

doi:10.1109/bmei.2009.5302220

Cited by 6 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Features are bag-of-words and bi, tri, and quadri-gram based. This feature setting follows Yu et al and Yang et al 37, 38 . The presence of relation triggers is also taken into account, using the previously described manually generated list.…”

Section: Methodsmentioning

confidence: 99%

Detecting miRNA Mentions and Relations in Biomedical Literature

et al. 2015

View full text Add to dashboard Cite

show abstract

Section: Methodsmentioning

confidence: 99%

Detecting miRNA Mentions and Relations in Biomedical Literature

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Most of the algorithms in the earlier literature were tested with the sets of the annotated data and labels, such as CoNLL‐2000, CoNLL‐2002, CoNLL‐2003 and Automatic Content Extraction (ACE; Finkel and Manning, ; Ratinov and Roth, ). A recent trend of studying NER is to replace annotated labels by sentiment expressions (Breck et al ., ), medical information (Aramaki et al ., ), gene–protein mentions (Airola et al ., , Yu et al ., ), Wikipedia (Wu and Weld, ) or even the whole Web (Whitelaw et al ., ). As far as the NER methods are considered, other than the rule‐based methods, the classification and sequential learning techniques have been paid special attentions in performing NER activities.…”

Section: Related Workmentioning

confidence: 99%

A Knowledge Engineering Framework for Identifying Key Impact Factors from Safety‐Related Accident Cases

Pan

Wang

et al. 2014

Syst. Res.

View full text Add to dashboard Cite

Consumer product safety closely relates to consumer health. In this paper, a knowledge engineering framework is proposed for data mining to identify key safety factors from a large number of consumer product safety cases. Data mining in the framework is performed in three steps. The first step is to collect consumer product safety cases, a case can be semistructured or unstructured, and cases can be collected either manually or automatically by a web spider crawling certain websites. The second step is to extract all safety factors from a number of consumer product safety cases. A new method based on linear chain conditional random field is developed to extract safety factors. The effectiveness of the method has been validated on product cases. The third step is to identify a set of key factors from all safety factors by knowledge reasoning. To illustrate the process of knowledge reasoning, a set of 3192 safety cases of electric products with electric shock accidents is chosen as the case study; a Bayesian network based model is developed to retrieve key safety factors relating to electric shock accidents. The performance of the reasoning model has been verified by a combination of experts' evaluation and experiments, and it has shown the proposed reasoning model can help identify key safety factors of electric shock accidents successfully. Overall, the proposed framework is capable of identifying key safety factors from a large number of consumer product safety cases. Copyright © 2014 John Wiley & Sons, Ltd.

show abstract

“…Each pixel was associated with a 6-dimensional feature vector generated by principal component analysis (PCA) on a 15 × 15 subimage centred on the pixel. Another example of a problem formated in a manner conducive to the proposed approach to discovering affinities in medical data is given by Yu et al [ 24 ] in terms of a protein-protein interaction extraction from biomedical text. Given an abstract of an article containing instances of proteins, the system detects whether a relationship exists for each pair of proteins in the abstract.…”

Section: Related Workmentioning

confidence: 99%

Arthritic Hand‐Finger Movement Similarity Measurements: Tolerance Near Set Approach

Henry

Peters

2011

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

The problem considered in this paper is how to measure the degree of resemblance between nonarthritic and arthritic hand movements during rehabilitation exercise. The solution to this problem stems from recent work on a tolerance space view of digital images and the introduction of image resemblance measures. The motivation for this work is both to quantify and to visualize differences between hand-finger movements in an effort to provide clinicians and physicians with indications of the efficacy of the prescribed rehabilitation exercise. The more recent introduction of tolerance near sets has led to a useful approach for measuring the similarity of sets of objects and their application to the problem of classifying image sequences extracted from videos showing finger-hand movement during rehabilitation exercise. The approach to measuring the resemblance between hand movement images introduced in this paper is based on an application of the well-known Hausdorff distance measure and a tolerance nearness measure. The contribution of this paper is an approach to measuring as well as visualizing the degree of separation between images in arthritic and nonarthritic hand-finger motion videos captured during rehabilitation exercise.

show abstract

Extracting Protein-Protein Interaction from Biomedical Text Using Additional Shallow Parsing Information

Cited by 6 publications

References 13 publications

Detecting miRNA Mentions and Relations in Biomedical Literature

Detecting miRNA Mentions and Relations in Biomedical Literature

A Knowledge Engineering Framework for Identifying Key Impact Factors from Safety‐Related Accident Cases

Arthritic Hand‐Finger Movement Similarity Measurements: Tolerance Near Set Approach

Contact Info

Product

Resources

About