Skip to main content
SpringerLink
Log in

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

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

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.

References

  1. Kim H, Chen S (2009) Associative naive Bayes classifier: automated linking of gene ontology to MEDLINE documents. Pattern Recogn 42:1777–1785

    Article  MATH  Google Scholar 

  2. French L, Pavlidis P (2012) Using text mining to link journal articles to neuroanatomical databases. J Comp Neurol 520(8):1772–1783

    Article  Google Scholar 

  3. Tsuruoka Y, Tsujii J, Ananiadou S (2008) FACTA: a text search engine for finding associated biomedical concepts. Bioinformatics 24(21):2559–2560

    Article  Google Scholar 

  4. Plake C, Schiemann T, Pankalla M, Hakenberg J, Leser U (2006) AliBaba: PubMed as a graph. Bioinformatics 22(19):2444–2445

    Article  Google Scholar 

  5. Doms A, Schroeder M (2005) GoPubMed: exploring PubMed with the gene ontology. Nucleic Acids Res 33:783–786

    Article  Google Scholar 

  6. Zhang M-L, Zhou Z-H (2007) ML-kNN: a lazy learning approach to multi-label learning. Pattern Recogn 40:2038–2048

    Article  MATH  Google Scholar 

  7. Godbole S, Sarawagi S (2004) Discriminative methods for multi-labeled classification. In: Dai H, Srikant R, Zhang C (eds) Lecture notes in artificial intelligence 3056. Springer, Berlin, pp 22–30

    Google Scholar 

  8. Zhang Y, Zhou Z-H (2008) Multi-label dimensionality reduction via dependency maximization. In: Proceedings of the 23rd AAAI conference on artificial intelligence, Chicago, IL, pp 1503–1505

  9. Zhang M-L, Zhou Z-H (2014) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng. http://doi.ieeecomputersociety.org/10.1109

  10. Xu J (2014) Multi-label core vector machine with a zero label. Pattern Recogn. doi:10.1016/j.patcog.2014.01.012

    MATH  Google Scholar 

  11. Boutell MR, Luo J, Shen X, Brown CM (2004) Learning multi-label scene classification. Pattern Recogn 37(9):1757–1771

    Article  Google Scholar 

  12. Schapire RE, Singer Y (2000) Boostexter: a boosting-based system for text categorization. Mach Learn 39(2/3):135–168

    Article  MATH  Google Scholar 

  13. Furnkranz J, Hullermeier E, Loza Mencía E, Brinker K (2008) Multilabel classification via calibrated label ranking. Machine Learning 73(2):133–153

    Article  Google Scholar 

  14. Tsoumakas G, Vlahavas I (2007) Random k-label sets: an ensemble method for multilabel classification. In: Kok JN, Koronacki J, de Mantaras RL, Matwin S, Mladenic D, Skowron A (eds) Lecture notes in artificial intelligence 4701. Springer, Berlin, pp 406–417

    Google Scholar 

  15. Zhou Z-H, Zhang M-L (2007) Multi-instance multi-label learning with application to scene classification. In: Scholkopf B, Platt J, Hoffman T (eds) Advances in neural information processing systems 19. MIT Press, Cambridge, pp 1609–1616

    Google Scholar 

  16. Clare A, King RD (2001) Knowledge discovery in multi-label phenotype data. In: De Raedt L, Siebes A (eds) Lecture notes in computer science 2168. Springer, Berlin, pp 42–53

    Google Scholar 

  17. Elisseeff A, Weston J (2002) A kernel method for multi-labelled classification. In: Dietterich TG, Becker S, Ghahramani Z (eds) Advances in neural information processing systems 14. MIT Press, Cambridge, pp 681–687

    Google Scholar 

  18. Zhang M-L, Zhou Z-H (2006) Multilabel neural networks with applications to functional genomics and text categorization. IEEE Trans Knowl Data Eng 18(10):1338–1351

    Article  Google Scholar 

  19. Fan R-E, Lin C-J (2007) A study on threshold selection for multi-label classification. National Taiwan University, Tech. Rep.

  20. Ioannou M, Sakkas G, Tsoumakas G, Vlahavas I (2010) Obtaining bipartition from score vectors for multi-label classification. In: Proceedings of the 22nd IEEE international conference on tools with artificial intelligence, Arras, France, pp 409–416

  21. Boutell MR, Luo J, Shen X, Brown CM (2004) Learning multi-label scene classification. Pattern Recogn 37(9):1757–1771

    Article  Google Scholar 

  22. Bi W, Kwok JT (2011) Multi-label classification on tree- and DAG-structured hierarchies. In: Proceedings of the 28th international conference on machine learning, Bellevue, WA, pp 17–24

  23. Brinker K (2005) On active learning in multi-label classification. In: Proceedings of the 29th annual conference of the German Classification Society, Magdeburg, Germany, pp 206–213

  24. Brinker K, Furnkranz J, Hullermeier E (2006) A unified model for multilabel classification and ranking. In: Proceedings of the 17th European conference on artificial intelligence, Riva del Garda, Italy, pp 489–493

  25. Quevedo JR, Luaces O, Bahamonde A (2012) Multilabel classifiers with a probabilistic thresholding strategy. Pattern Recogn 45(2):876–883

    MATH  Google Scholar 

  26. Zhang M-L, Peña JM, Robles V (2009) Feature selection for multi-label naive Bayes classification. Inf Sci 179(19):3218–3229

    Article  MATH  Google Scholar 

  27. Poggio T, Mukherjee S, Rifkin R, Rakhlin A, Verri A (2001) ‘‘b,’’ A.I. Memo No. 2001-011, CBCL Memo 198, Artificial Intelligence Laboratory, Massachusetts Institute of Technology

  28. Chen B, Zhao S, Zhu P, Principe JC (2012) Quantized kernel least mean square algorithm. IEEE Trans Neural Netw Learn Syst 23(1):22–32

    Article  Google Scholar 

  29. De Brabanter K, De Brabanter J, Suykens JAK, De Moor B (2010) Optimized fixed-size kernel models for large data sets. Comput Stat Data Anal 54(6):1484–1504

    Article  MathSciNet  MATH  Google Scholar 

  30. Chen B, Zhao S, Zhu P, Principe JC (2013) Quantized kernel recursive least squares algorithm. IEEE Trans Neural Netw Learn Syst 24(9):1484–1491

    Article  Google Scholar 

  31. Guo Y, Schuurmans D (2011) Adaptive large margin training for multilabel classification. In: Proceedings of the 25th AAAI conference on artificial intelligence, San Francico, CA, pp 374–379

  32. Jiang A, Wang C, Zhu Y (2008) Calibrated rank-svm for multi-label image categorization. In: Proceedings of the international joint conference on neural networks, Hong Kong, pp 1450–1455

  33. Ji S, Sun L, Jin R, Ye J (2009) Multi-label multiple kernel learning. In: Koller D, Schuurmans D, Bengio Y, Bottou L (eds) Advances in neural information processing systems 21. MIT Press, Cambridge, pp 777–784

    Google Scholar 

  34. Elisseeff A, Weston J (2002) A kernel method for multi-labelled classification. Adv Neural Inf Process Syst 14:681–687

    Google Scholar 

  35. Xu J (2013) Fast multi-label core vector machine. Pattern Recogn 46(3):885–898

    Article  MATH  Google Scholar 

  36. Xu J (2012) An efficient multi-label support vector machine with a zero label. Expert Syst Appl 39(5):4796–4804

    Article  Google Scholar 

  37. Huang G-B, Xhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern 42(2):513–529

    Article  Google Scholar 

  38. Ding X-J, Zhao Y-L (2011) Influence of bias b on generalization ability of SVM for classification. Acta Autom Sin 37(9):1105–1113

    MathSciNet  Google Scholar 

Download references

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).

Author information

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

Authors
  1. Xia Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiarong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Su-Shing Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Feijuan He
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xia Sun.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

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

Keywords

Search

Navigation