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CoIN: a network analysis for document triage.

Hsu YY, Kao HY - Database (Oxford) (2013)

Bottom Line: Under these circumstances, a system that can automatically determine in advance which article has a higher priority for curation can effectively reduce the workload of biocurators.Determining how to effectively find the articles required by biocurators has become an important task.The experimental results show that our network-based approach combined with co-occurrence features can effectively classify curatable and non-curatable articles.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan, R.O.C. (Republic of China).

ABSTRACT
In recent years, there was a rapid increase in the number of medical articles. The number of articles in PubMed has increased exponentially. Thus, the workload for biocurators has also increased exponentially. Under these circumstances, a system that can automatically determine in advance which article has a higher priority for curation can effectively reduce the workload of biocurators. Determining how to effectively find the articles required by biocurators has become an important task. In the triage task of BioCreative 2012, we proposed the Co-occurrence Interaction Nexus (CoIN) for learning and exploring relations in articles. We constructed a co-occurrence analysis system, which is applicable to PubMed articles and suitable for gene, chemical and disease queries. CoIN uses co-occurrence features and their network centralities to assess the influence of curatable articles from the Comparative Toxicogenomics Database. The experimental results show that our network-based approach combined with co-occurrence features can effectively classify curatable and non-curatable articles. CoIN also allows biocurators to survey the ranking lists for specific queries without reviewing meaningless information. At BioCreative 2012, CoIN achieved a 0.778 mean average precision in the triage task, thus finishing in second place out of all participants. Database URL: http://ikmbio.csie.ncku.edu.tw/coin/home.php.

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The input screen of CoIN.
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bat076-F3: The input screen of CoIN.

Mentions: For example, we use the PubMed articles for phenacetin as an input list; otherwise, the user can input a gene, disease or chemical name, as shown in Figure 3. After the computation is finished, we can obtain a ranking list, as shown in Figure 4. The name recognition process is usually time-consuming for the system schema of CoIN. CoIN provides a quick sorting result to biocurators after the name recognition process is finished. CoIN takes less time to train complex features, but the system immediately returns the ranking result from the network centralities of co-occurrence networks.Figure 3.


CoIN: a network analysis for document triage.

Hsu YY, Kao HY - Database (Oxford) (2013)

The input screen of CoIN.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3822784&req=5

bat076-F3: The input screen of CoIN.
Mentions: For example, we use the PubMed articles for phenacetin as an input list; otherwise, the user can input a gene, disease or chemical name, as shown in Figure 3. After the computation is finished, we can obtain a ranking list, as shown in Figure 4. The name recognition process is usually time-consuming for the system schema of CoIN. CoIN provides a quick sorting result to biocurators after the name recognition process is finished. CoIN takes less time to train complex features, but the system immediately returns the ranking result from the network centralities of co-occurrence networks.Figure 3.

Bottom Line: Under these circumstances, a system that can automatically determine in advance which article has a higher priority for curation can effectively reduce the workload of biocurators.Determining how to effectively find the articles required by biocurators has become an important task.The experimental results show that our network-based approach combined with co-occurrence features can effectively classify curatable and non-curatable articles.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan, R.O.C. (Republic of China).

ABSTRACT
In recent years, there was a rapid increase in the number of medical articles. The number of articles in PubMed has increased exponentially. Thus, the workload for biocurators has also increased exponentially. Under these circumstances, a system that can automatically determine in advance which article has a higher priority for curation can effectively reduce the workload of biocurators. Determining how to effectively find the articles required by biocurators has become an important task. In the triage task of BioCreative 2012, we proposed the Co-occurrence Interaction Nexus (CoIN) for learning and exploring relations in articles. We constructed a co-occurrence analysis system, which is applicable to PubMed articles and suitable for gene, chemical and disease queries. CoIN uses co-occurrence features and their network centralities to assess the influence of curatable articles from the Comparative Toxicogenomics Database. The experimental results show that our network-based approach combined with co-occurrence features can effectively classify curatable and non-curatable articles. CoIN also allows biocurators to survey the ranking lists for specific queries without reviewing meaningless information. At BioCreative 2012, CoIN achieved a 0.778 mean average precision in the triage task, thus finishing in second place out of all participants. Database URL: http://ikmbio.csie.ncku.edu.tw/coin/home.php.

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