uu.seUppsala University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Application of conformal prediction in QSAR
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
2012 (English)In: Artificial Intelligence Applications and Innovations: AIAI 2012 International Workshops: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Halkidiki, Greece, September 27-30, 2012, Proceedings, Part II / [ed] Lazaros Iliadis, Ilias Maglogiannis, Harris Papadopoulos, Kostas Karatzas, Spyros Sioutas, 2012, no PART 2, 166-175 p.Conference paper, Published paper (Refereed)
Abstract [en]

QSAR modeling is a method for predicting properties, e.g. the solubility or toxicity, of chemical compounds using statistical learning techniques. QSAR is in widespread use within the pharmaceutical industry to prioritize compounds for experimental testing or to alert for potential toxicity. However, predictions from a QSAR model are difficult to assess if their prediction intervals are unknown. In this paper we introduce conformal prediction into the QSAR field to address this issue. We apply support vector machine regression in combination with two nonconformity measures to five datasets of different sizes to demonstrate the usefulness of conformal prediction in QSAR modeling. One of the nonconformity measures provides prediction intervals with almost the same width as the size of the QSAR models' prediction errors, showing that the prediction intervals obtained by conformal prediction are efficient and useful.

Place, publisher, year, edition, pages
2012. no PART 2, 166-175 p.
Series
IFIP Advances in Information and Communication Technology, ISSN 1868-4238 ; 382 AICT
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-193891DOI: 10.1007/978-3-642-33412-2_17ISBN: 9783642334115 (print)OAI: oai:DiVA.org:uu-193891DiVA: diva2:604919
Conference
8th International Workshop on Artificial Intelligence Applications and Innovations, AIAI 2012: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, 27 September 2012 through 30 September 2012, Halkidiki
Available from: 2013-02-12 Created: 2013-02-06 Last updated: 2013-02-12Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Authority records BETA

Eklund, Martin

Search in DiVA

By author/editor
Eklund, Martin
By organisation
Department of Pharmaceutical Biosciences
Medical and Health Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 368 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf