Skip to main content

Interpretation of Conformal Prediction Classification Models

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9047))

Abstract

We present a method for interpretation of conformal prediction models. The discrete gradient of the largest p-value is calculated with respect to object space. A criterion is applied to identify the most important component of the gradient and the corresponding part of the object is visualized.

The method is exemplified with data from drug discovery relating chemical compounds to mutagenicity. Furthermore, a comparison is made to already established important subgraphs with respect to mutagenicity and this initial assessment shows very useful results with respect to interpretation of a conformal predictor.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Daylight Theory: SMARTS - A Language for Describing Molecular Patterns. http://www.daylight.com/dayhtml/doc/theory/theory.smarts.html (accessed January 13, 2015)

  2. Openeye Scientific Software. http://www.eyesopen.com (accessed August 30, 2014)

  3. Ames, B.N., Lee, F.D., Durston, W.E.: An improved bacterial test system for the detection and classification of mutagens and carcinogens. Proceedings of the National Academy of Sciences 70(3), 782–786 (1973). http://www.pnas.org/content/70/3/782.abstract

  4. Carlsson, L., Helgee, E.A., Boyer, S.: Interpretation of nonlinear qsar models applied to ames mutagenicity data. Journal of Chemical Information and Modeling 49(11), 2551–2558 (2009). http://dx.doi.org/10.1021/ci9002206, pMID: 19824682

  5. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

  6. Eklund, M., Norinder, U., Boyer, S., Carlsson, L.: The application of conformal prediction to the drug discovery process. Annals of Mathematics and Artificial Intelligence, pp. 1–16 (2013). http://dx.doi.org/10.1007/s10472-013-9378-2

  7. Faulon, J.L., Churchwell, C.J.: Signature Molecular Descriptor. 2. Enumerating Molecules from Their Extended Valence Sequences. J. Chem. Inf. Comput. Sci. 43, 721–734 (2003)

    Article  Google Scholar 

  8. Faulon, J.L., Visco, D.P.J., Pophale, R.S.: Signature Molecular Descriptor. 1. Using Extended Valence Sequences in QSAR and QSPR Studies. J. Chem. Inf. Comput. Sci. 43, 707–720 (2003)

    Article  Google Scholar 

  9. Grover, M., Singh, B., Bakshi, M., Singh, S.: Quantitative structure-property relationships in pharmaceutical research. Pharm. Sci. & Tech. Today 3(1), 28–35 (2000)

    Article  Google Scholar 

  10. Kazius, J., McGuire, R., Bursi, R.: Derivation and Validation of Toxicophores for Mutagenicity Prediction. J. Med. Chem 48, 312–320 (2005)

    Article  Google Scholar 

  11. Lewis, R.A.: A General Method for Exploiting QSAR Models in Lead Optimization. J. Med. Chem. 48(5), 1638–1648 (2005)

    Article  Google Scholar 

  12. Shafer, G., Vovk, V.: A tutorial on conformal prediction. Journal of Machine Learning Research 9, 371–421 (2008). http://www.jmlr.org/papers/volume9/shafer08a/shafer08a.pdf

  13. Spjuth, O., Eklund, M., Ahlberg Helgee, E., Boyer, S., Carlsson, L.: Integrated decision support for assessing chemical liabilities. J. Chem. Inf. Model. 51(8), 1840–1847 (2011)

    Google Scholar 

  14. Steinbeck, C., Han, Y., Kuhn, S., Horlacher, O., Luttmann, E., Willighagen, E.: The chemistry development kit (cdk) an open-source java library for chemo- and bioinformatics. J. Chem. Inf. Comput. Sci. 43(2), 493–500 (2003). http://dx.doi.org/10.1021/ci025584y, pMID: 12653513

  15. Stålring, J., Almeida, P.R., Carlsson, L., Helgee Ahlberg, E., Hasselgren, C., Boyer, S.: Localized heuristic inverse quantitative structure activity relationship with bulk descriptors using numerical gradients. Journal of Chemical Information and Modeling 53(8), 2001–2017 (2013). http://dx.doi.org/10.1021/ci400281y, pMID: 23845139

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

    MATH  Google Scholar 

  17. Young, S., Gombar, V., Emptage, M., Cariello, N., Lambert, C.: Mixture De-Convolution and Analysis of Ames Mutagenicity Data. Chemometrics and Intelligent Laboratory Systems 60, 5–11 (2002)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lars Carlsson .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Ahlberg, E., Spjuth, O., Hasselgren, C., Carlsson, L. (2015). Interpretation of Conformal Prediction Classification Models. In: Gammerman, A., Vovk, V., Papadopoulos, H. (eds) Statistical Learning and Data Sciences. SLDS 2015. Lecture Notes in Computer Science(), vol 9047. Springer, Cham. https://doi.org/10.1007/978-3-319-17091-6_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-17091-6_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17090-9

  • Online ISBN: 978-3-319-17091-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics