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Symposium on Conformal and Probabilistic Prediction with Applications

COPA 2016: Conformal and Probabilistic Prediction with Applications pp 51–66Cite as

Conformal Predictors for Compound Activity Prediction

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9653))

Abstract

The paper presents an application of Conformal Predictors to a chemoinformatics problem of identifying activities of chemical compounds. The paper addresses some specific challenges of this domain: a large number of compounds (training examples), high-dimensionality of feature space, sparseness and a strong class imbalance. A variant of conformal predictors called Inductive Mondrian Conformal Predictor is applied to deal with these challenges. Results are presented for several non-conformity measures (NCM) extracted from underlying algorithms and different kernels. A number of performance measures are used in order to demonstrate the flexibility of Inductive Mondrian Conformal Predictors in dealing with such a complex set of data.

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Notes

  1. 1.

    The signature descriptors and other types of descriptors (e.g. circular descriptors) can be computed with the CDK Java package or any of its adaptations such as the RCDK package for the R statistical software.

  2. 2.

    In the case of linear SVM, it is possible to tackle the formulation of the quadratic optimization problem at the heart of the SVM in the primal and solve it with techniques such as Stochastic Gradient Descent or L-BFGS, which lend themselves well to being distributed across an array of computational nodes.

  3. 3.

    See [8] for a proof that Tanimoto Similarity is a kernel.

  4. 4.

    According to https://www.sgi.com/company_info/newsroom/press_releases/2015/september/salomon.html.

References

  1. Monve, V.: Introduction to similarity searching in chemistry. MATCH - Comm. Math. Comp. Chem. 51, 7–38 (2004)

    MathSciNet  Google Scholar 

  2. Bottou, L., Chapelle, O., DeCoste, D., Weston, J.: Large-Scale Kernel Machines (Neural Information Processing). The MIT Press, Cambridge (2007)

    Google Scholar 

  3. Bussonnier, M.: Interactive parallel computing in Python. https://github.com/ipython/ipyparallel

  4. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). http://www.csie.ntu.edu.tw/~jlin/libsvm

    Google Scholar 

  5. Chang, E.Y.: PSVM: parallelizing support vector machines on distributed computers. Foundations of Large-Scale Multimedia Information Management and Retrieval, pp. 213–230. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  6. Faulon Jr., J.-L., Visco, D.P., Pophale, R.S.: The signature molecular descriptor. 1. Using extended valence sequences in QSAR and QSPR studies. J. Chem. Inf. Comput. Sci. 43(3), 707–720 (2003)

    Article  Google Scholar 

  7. Gammerman, A., Vovk, V.: Hedging predictions in machine learning. Comput. J. 50(2), 151–163 (2007)

    Article  Google Scholar 

  8. Gärtner, T.: Kernels For Structured Data. World Scientific Publishing Co. Inc., River Edge (2009)

    MATH  Google Scholar 

  9. Graf, H.P., Cosatto, E., Bottou, L., Durdanovic, I., Vapnik, V.: Parallel Support Vector Machines: The Cascade SVM. In: Saul, L.K., Weiss, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems, pp. 521–528. MIT Press, Cambridge (2005)

    Google Scholar 

  10. Jain, A.N., Nicholls, A.: Recommendations for evaluation of computational methods. J. Comput. Aided Mol. Des. 22(3–4), 133–139 (2008)

    Article  Google Scholar 

  11. Shafer, G., Vovk, V.: A tutorial on conformal prediction. J. Mach. Learn. Res. 9, 371–421 (2008)

    MathSciNet  MATH  Google Scholar 

  12. Vovk, V., Gammerman, A., Shafer, G.: Algorithmic Learning in a Random World. Springer-Verlag New York, Inc., Secaucus (2005)

    MATH  Google Scholar 

  13. Weis, D.C., Visco Jr., D.P., Faulon, J.-L.: Data mining pubchem using a support vector machine with the signature molecular descriptor: classification of factor XIa inhibitors. J. Mol. Graph. Model. 27(4), 466–475 (2008)

    Article  Google Scholar 

  14. Woodsend, K., Gondzio, J.: Hybrid MPI/OpenMP parallel linear support vector machine training. J. Mach. Learn. Res. 10, 1937–1953 (2009)

    MathSciNet  MATH  Google Scholar 

  15. You, Y., Fu, H., Song, S.L., Randles, A., Kerbyson, D., Marquez, A., Yang, G., Hoisie, A.: Scaling support vector machines on modern HPC platforms. J. Parallel Distrib. Comput. 76(C), 16–31 (2015)

    Article  Google Scholar 

  16. Toccaceli, P., Nouretdinov, I., Luo, Z., Vovk, V., Carlsson, L., Gammerman, A.: Conformal predictors. Technical report for EU Horizon 2020 Programme ExCape Project. Royal Holloway, London, December 2015

    Google Scholar 

  17. Carlsson, L., Ahlberg, E., Boström, H., Johansson, U., Linusson, H.: Modifications to p-values of conformal predictors. In: SLDS 2015, pp. 251–259

    Google Scholar 

  18. Nouretdinov, I., Gammerman, A., Qi, Y., Klein-Seetharaman, J.: Determining confidence of predicted interactions between HIV-1 and human proteins using conformal method. In: Pacific Symposium on Biocomputing, p. 311 (2012)

    Google Scholar 

  19. Wang, Y., Suzek, T., Zhang, J., Wang, J., He, S., Cheng, T., Shoemaker, B.A., Gindulyte, A., Bryant, S.H.: PubChem BioAssay: 2014 update. Nucleic Acids Res. 42(1), D1075–D1082 (2014)

    Article  Google Scholar 

  20. McCool, M., Robison, A.D., Reinders, J.: Structured Parallel Programming: Patterns for Efficient Computation. Morgan-Kaufmann, Burlington (2012)

    Google Scholar 

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Acknowledgments

This project (ExCAPE) has received funding from the European Unions Horizon 2020 Research and Innovation programme under Grant Agreement no. 671555. We are grateful for the help in conducting experiments to the Ministry of Education, Youth and Sports (Czech Republic) that supports the Large Infrastructures for Research, Experimental Development and Innovations project “IT4Innovations National Supercomputing Center LM2015070”. This work was also supported by EPSRC grant EP/K033344/1 (“Mining the Network Behaviour of Bots”). We are indebted to Lars Carlsson of Astra Zeneca for providing the data and useful discussions. We are also thankful to Zhiyuan Luo and Vladimir Vovk for many valuable comments and discussions.

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Authors and Affiliations

  1. Royal Holloway, University of London, Egham, UK

    Paolo Toccaceli, Ilia Nouretdinov & Alexander Gammerman

Authors
  1. Paolo Toccaceli
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  2. Ilia Nouretdinov
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  3. Alexander Gammerman
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Corresponding authors

Correspondence to Paolo Toccaceli , Ilia Nouretdinov or Alexander Gammerman .

Editor information

Editors and Affiliations

  1. University of London, Egham, United Kingdom

    Alexander Gammerman

  2. University of London, Egham, United Kingdom

    Zhiyuan Luo

  3. CIEMAT, Madrid, Spain

    Jesús Vega

  4. University of London, Egham, United Kingdom

    Vladimir Vovk

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Toccaceli, P., Nouretdinov, I., Gammerman, A. (2016). Conformal Predictors for Compound Activity Prediction. In: Gammerman, A., Luo, Z., Vega, J., Vovk, V. (eds) Conformal and Probabilistic Prediction with Applications. COPA 2016. Lecture Notes in Computer Science(), vol 9653. Springer, Cham. https://doi.org/10.1007/978-3-319-33395-3_4

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  • DOI: https://doi.org/10.1007/978-3-319-33395-3_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33394-6

  • Online ISBN: 978-3-319-33395-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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