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Classifying biomedical knowledge in PubMed using multi-label vector machines with weaker optimization constraints

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Abstract

In this paper, we have developed an automated multi-label linking scheme for PubMed citations with gene ontology (GO) terms, which enables users to have easy access to relevant publications according to various biomedical ontological terms (in particular, GO terms). We propose a maximum margin approach derived from ranking support vector machine (Rank-SVM), called SCRank-SVM. In this scheme, we remove the term bias “b” and recast the decision boundary and the separating margin to improve the margin of Rank-SVM. Due to the weaker optimization constraints, SCRank-SVM has better generalization performance and lower computational complexity. Experiments on our lung cancer data set and 6 diverse multi-label data sets show that SCRank-SVM is quite suitable to solve our problem. The performance of SCRank-SVM is superior to that of the original Rank-SVM and some other well-established multi-label learning algorithms.

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Notes

  1. [1] has surveyed the top 20 most frequently occurring GO terms in MEDLINE citations.

  2. Limiting the results to the MeSH major topic field with lung cancer, which is different from the queries in Ref. [1], resulted in only 16 top GO terms to be found in MEDLINE citations.

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Acknowledgments

The authors wish to thank the anonymous reviewers for their helpful comments and suggestions. The author also thanks Prof. Zhihua Zhou, Mingling Zhang and Jianhua Xu, whose software and data have been used in our experiments. This work was supported by NSFc (Grant No. 61202184) and Natural Science Basic Research Plan in Shaanxi Province of China (No. 2015JQ6240).

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Authors and Affiliations

  1. School of Information Science and Technology, Northwest University, Xi’an, 710069, China

    Xia Sun, Jiarong Wang & Jun Feng

  2. Systems Biology Lab, University of Florida, Gainesville, FL, 32608, USA

    Xia Sun & Su-Shing Chen

  3. Computer Information Science and Engineering, University of Florida, Gainesville, FL, 32608, USA

    Su-Shing Chen

  4. Department of Computer Science, Xi’an Jiaotong University City College, Xi’an, 710069, China

    Feijuan He

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  1. Xia Sun
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Correspondence to Xia Sun.

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Sun, X., Wang, J., Feng, J. et al. Classifying biomedical knowledge in PubMed using multi-label vector machines with weaker optimization constraints. Neural Comput & Applic 28 (Suppl 1), 1233–1243 (2017). https://doi.org/10.1007/s00521-016-2439-9

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  • DOI: https://doi.org/10.1007/s00521-016-2439-9

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