Overview of the BioCreative VI text-mining services for Kinome Curation Track

Gobeill, Julien; Gaudet, Pascale; Dopp, Daniel; Morrone, Adam; Kahanda, Indika; Hsu, Yi-Yu; Wei, Chih-Hsuan; Lu, Zhiyong; Ruch, Patrick

doi:10.1093/database/bay104

Cited by 5 publications

(6 citation statements)

References 20 publications

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“…Then the above-retrieved articles were provided to three annotators for curation (an example of the annotation guidelines used is available online 11 ). For each construct, they were asked to read the title and the abstract and determine whether it provides enough evidence that the abstract was related to the construct.…”

Section: The Benchmark Preparationmentioning

confidence: 99%

“…In our dataset, at most 6 sentences were observed. This consoli- 11 https://montana.box.com/s/kh0hmyn1jcj5ajvr2nibq4iw wgiv3led dated data was used to create training and test sets as described below.…”

Section: The Benchmark Preparationmentioning

confidence: 99%

“…Then the above-retrieved articles were provided to three annotators for curation (an example of the annotation guidelines used is available online 11 ).…”

Section: The Benchmark Preparationmentioning

confidence: 99%

“…For task 1, we use Mean Average Precision (MAP) [16] as the performance measure because it is one of the most frequently used measures for IR [31,8,11]. First, we compute the Average Precision (AP) for each construct independently and macro-average across the constructs to compute the Mean Average Precision.…”

Section: Metrics Used For Evaluationmentioning

confidence: 99%

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RDoC Task at BioNLP-OST 2019

Anani

Kazi

Kuntz

et al. 2019

Proceedings of the 5th Workshop on BioNLP Open Shared Tasks

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BioNLP Open Shared Tasks (BioNLP-OST) is an international competition organized to facilitate development and sharing of computational tasks of biomedical text mining and solutions to them. For BioNLP-OST 2019, we introduced a new mental health informatics task called "RDoC Task", which is composed of two subtasks: information retrieval and sentence extraction through National Institutes of Mental Health's Research Domain Criteria framework. Five and four teams around the world participated in the two tasks, respectively. According to the performance on the two tasks, we observe that there is room for improvement for text mining on brain research and mental illness.

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Section: The Benchmark Preparationmentioning

confidence: 99%

Section: The Benchmark Preparationmentioning

confidence: 99%

“…Then the above-retrieved articles were provided to three annotators for curation (an example of the annotation guidelines used is available online 11 ).…”

Section: The Benchmark Preparationmentioning

confidence: 99%

Section: Metrics Used For Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

RDoC Task at BioNLP-OST 2019

Anani

Kazi

Kuntz

et al. 2019

Proceedings of the 5th Workshop on BioNLP Open Shared Tasks

Self Cite

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“…The human kinome curation track includes three subtasks: (i) abstract triage, (ii) full text triage and (iii) snippet selection. The task organizers (which included the neXtProt team) provided an as yet unpublished data set for the task ( 10 ).…”

Section: Introductionmentioning

confidence: 99%

Assisting document triage for human kinome curation via machine learning

Hsu

Wei

2018

Database

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In the era of data explosion, the increasing frequency of published articles presents unorthodox challenges to fulfill specific curation requirements for bio-literature databases. Recognizing these demands, we designed a document triage system with automatic methods that can improve efficiency to retrieve the most relevant articles in curation workflows and reduce workloads for biocurators. Since the BioCreative VI (2017), we have implemented texting mining processing in our system in hopes of providing higher effectiveness for curating articles related to human kinase proteins. We tested several machine learning methods together with state-of-the-art concept extraction tools. For features, we extracted rich co-occurrence and linguistic information to model the curation process of human kinome articles by the neXtProt database. As shown in the official evaluation on the human kinome curation task in BioCreative VI, our system can effectively retrieve 5.2 and 6.5 kinase articles with the relevant disease (DIS) and biological process (BP) information, respectively, among the top 100 returned results. Comparing to neXtA5, our system demonstrates significant improvements in prioritizing kinome-related articles as follows: our system achieves 0.458 and 0.109 for the DIS axis whereas the neXtA5’s best-reported mean average precision (MAP) and maximum precision observed are 0.41 and 0.04. Our system also outperforms the neXtA5 in retrieving BP axis with 0.195 for MAP and the neXtA5’s reported value was 0.11. These results suggest that our system may be able to assist neXtProt biocurators in practice.

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Dictionary-based matching graph network for biomedical named entity recognition

Lou,

Zhu,

Tan

2023

Sci Rep

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Biomedical named entity recognition (BioNER) is an essential task in biomedical information analysis. Recently, deep neural approaches have become widely utilized for BioNER. Biomedical dictionaries, implemented through a masked manner, are frequently employed in these methods to enhance entity recognition. However, their performance remains limited. In this work, we propose a dictionary-based matching graph network for BioNER. This approach utilizes the matching graph method to project all possible dictionary-based entity combinations in the text onto a directional graph. The network is implemented coherently with a bi-directional graph convolutional network (BiGCN) that incorporates the matching graph information. Our proposed approach fully leverages the dictionary-based matching graph instead of a simple masked manner. We have conducted numerous experiments on five typical Bio-NER datasets. The proposed model shows significant improvements in F1 score compared to the state-of-the-art (SOTA) models: 2.8% on BC2GM, 1.3% on BC4CHEMD, 1.1% on BC5CDR, 1.6% on NCBI-disease, and 0.5% on JNLPBA. The results show that our model, which is superior to other models, can effectively recognize natural biomedical named entities.

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Overview of the BioCreative VI text-mining services for Kinome Curation Track

Cited by 5 publications

References 20 publications

RDoC Task at BioNLP-OST 2019

RDoC Task at BioNLP-OST 2019

Assisting document triage for human kinome curation via machine learning

Dictionary-based matching graph network for biomedical named entity recognition

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