Self organising hypothesis networks: a new approach for representing and structuring SAR knowledge-Reference-Cited by-同舟云学术

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Self organising hypothesis networks: a new approach for representing and structuring SAR knowledge

Published:2014-05-08 Issue:1 Volume:6 Page:
ISSN:1758-2946
Container-title:Journal of Cheminformatics
language:en
Short-container-title:J Cheminform

Author:

Hanser Thierry,Barber Chris,Rosser Edward,Vessey Jonathan D,Webb Samuel J,Werner Stéphane

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Computer Graphics and Computer-Aided Design,Physical and Theoretical Chemistry,Computer Science Applications

Link

http://link.springer.com/article/10.1186/1758-2946-6-21/fulltext.html

Reference33 articles.

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2. Cooper JA, Saracci R, Cole P: Describing the validity of carcinogen screening tests. Br J Cancer. 1979, 39 (1): 87-89. 10.1038/bjc.1979.10.

3. Chen H, Carlsson L, Eriksson M, Varkonyi P, Norinder U, Nilsson I: Beyond the scope of free-Wilson analysis: building interpretable QSAR models with machine learning algorithms. J Chem Inf Model. 2013, 53 (6): 1324-1336. 10.1021/ci4001376.

4. Carlsson L, Helgee EA, Boyer S: Interpretation of nonlinear QSAR models applied to ames mutagenicity data. J Chem Inf Model. 2009, 49 (11): 2551-2558. 10.1021/ci9002206.

5. Rosenbaum L, Hinselmann G, Jahn A, Zell A: Interpreting linear support vector machine models with heat map molecule coloring. J Cheminformatics. 2011, 3 (1): 11-10.1186/1758-2946-3-11.

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