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Abstract
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| This article presents a new approach based on building several prediction models for the Bioinformatics journal. These models predict the citation count of an article within 4 years after publication (global models). To build these models, tokens found in the abstracts of Bioinformatics papers have been used as predictive features, along with other features like the journal sections and 2-week post-publication periods. To improve the accuracy of the global models, specific models have been built for each Bioinformatics journal section (Data and Text Mining, Databases and Ontologies, Gene Expression, Genetics and Population Analysis, Genome Analysis, Phylogenetics, Sequence Analysis, Structural Bioinformatics and Systems Biology). In these new models, the average success rate for predictions using the naive Bayes and logistic regression supervised classification methods was 89.4% and 91.5%, respectively, within the nine sections and for 4-year time horizon. | |
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International
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Si |
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JCR
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Si |
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Title
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BIOINFORMATICS |
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ISBN
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1367-4803 |
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Impact factor JCR
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4,328 |
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Impact info
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Volume
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25 |
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Journal number
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24 |
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From page
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3303 |
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To page
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3309 |
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Month
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DICIEMBRE |
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Ranking
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