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Improving chemical disease relation extraction with rich features and weakly labeled data

Overview of attention for article published in Journal of Cheminformatics, October 2016
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Mentioned by

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6 tweeters

Citations

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34 Dimensions

Readers on

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58 Mendeley
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1 CiteULike
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Title
Improving chemical disease relation extraction with rich features and weakly labeled data
Published in
Journal of Cheminformatics, October 2016
DOI 10.1186/s13321-016-0165-z
Pubmed ID
Authors

Yifan Peng, Chih-Hsuan Wei, Zhiyong Lu

Abstract

Due to the importance of identifying relations between chemicals and diseases for new drug discovery and improving chemical safety, there has been a growing interest in developing automatic relation extraction systems for capturing these relations from the rich and rapid-growing biomedical literature. In this work we aim to build on current advances in named entity recognition and a recent BioCreative effort to further improve the state of the art in biomedical relation extraction, in particular for the chemical-induced disease (CID) relations. We propose a rich-feature approach with Support Vector Machine to aid in the extraction of CIDs from PubMed articles. Our feature vector includes novel statistical features, linguistic knowledge, and domain resources. We also incorporate the output of a rule-based system as features, thus combining the advantages of rule- and machine learning-based systems. Furthermore, we augment our approach with automatically generated labeled text from an existing knowledge base to improve performance without additional cost for corpus construction. To evaluate our system, we perform experiments on the human-annotated BioCreative V benchmarking dataset and compare with previous results. When trained using only BioCreative V training and development sets, our system achieves an F-score of 57.51 %, which already compares favorably to previous methods. Our system performance was further improved to 61.01 % in F-score when augmented with additional automatically generated weakly labeled data. Our text-mining approach demonstrates state-of-the-art performance in disease-chemical relation extraction. More importantly, this work exemplifies the use of (freely available) curated document-level annotations in existing biomedical databases, which are largely overlooked in text-mining system development.

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 58 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 36%
Student > Master 10 17%
Researcher 6 10%
Student > Bachelor 4 7%
Student > Doctoral Student 4 7%
Other 4 7%
Unknown 9 16%
Readers by discipline Count As %
Computer Science 28 48%
Agricultural and Biological Sciences 4 7%
Engineering 3 5%
Chemistry 2 3%
Medicine and Dentistry 2 3%
Other 7 12%
Unknown 12 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 December 2016.
All research outputs
#5,855,962
of 11,350,788 outputs
Outputs from Journal of Cheminformatics
#287
of 444 outputs
Outputs of similar age
#100,691
of 260,012 outputs
Outputs of similar age from Journal of Cheminformatics
#16
of 24 outputs
Altmetric has tracked 11,350,788 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 444 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.7. This one is in the 34th percentile – i.e., 34% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 260,012 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.