The INFUSIS Project

Henrik Boström
Informatics Research Centre, University of Skövde, Sweden \ Dept. Of Computer and Systems Sciences, Stockholm University, Sweden

Ulf Norinder
AstraZeneca R&D Södertälje, Sweden

Ulf Johansson
School of Business and Informatics, University of Borås, Sweden

Cecilia Sönströd
School of Business and Informatics, University of Borås, Sweden

Tuve Löfström
School of Business and Informatics, University of Borås, Sweden

Elzbieta Dura
Lexware Labs, Sweden

Ola Engkvist
AstraZeneca R&D Mölndal, Sweden

Sorel Muresan
AstraZeneca R&D Mölndal, Sweden

Niklas Blomberg
AstraZeneca R&D Mölndal, Sweden

Ladda ner artikel

Ingår i: The Swedish AI Society Workshop May 20-21; 2010; Uppsala University

Linköping Electronic Conference Proceedings 48:11, s. 65-70

Visa mer +

Publicerad: 2010-05-19


ISSN: 1650-3686 (tryckt), 1650-3740 (online)


The INFUSIS project is a three-year colla-boration between industry and academia in order to further the development of new effective methods for generating predictive and interpretable models from machine learning and text mining to solve drug discovery problems.


Inga nyckelord är tillgängliga


[1] H. van de Waterbeemd and E. Gifford; “Admet in silico modelling: towards prediction paradise?” Nat Rev Drug Discov; vol. 2; no. 3; pp 192–204; 2003.

[2] H. Boström and U. Norinder; “Utilizing Information on Uncertainty for In Silico Modeling using Random Forests”; Proc. of the 3rd Skövde Workshop on Information Fusion Topics; pp 59-62; 2009.

[3] C. Dudas and H. Boström; “Using uncertain chemical and thermal data to predict product quality in a casting process”; Proc. of the First ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data; pp 57–61; 2009.

[4] U. Johansson; C. Sönströd; T. Löfström and H. Boström; “Chipper – A Novel Algorithm for Concept Description”; Proc. of the Scandinavian Conference on Artificial Intelligence; pp 133-140; 2008.

[5] C. Sönströd; U. Johansson; U. Norinder; and H. Boström; “Comprehensible Models for Predicting Molecular Interaction with Heart-Regulating Genes”; Proc. of the Inter-national Conference on Machine Learning and Applications; pp 559 – 564; 2008.

[6] C. Sönströd; U. Johansson and U. Norinder; “Generating Comprehensible QSAR models”; Proc. of the 3rd Skövde Workshop on Information Fusion Topics; pp 44-48; 2009.

[7] U. Johansson; R. König and L. Niklasson; “Rule Extraction from Trained Neural Networks using Genetic Programming”; Proc. of the International Conference on Artificial Neural Networks; supplementary proceedings; pp 13-16; 2003.

[8] U. Johansson and L. Niklasson; “Evolving Decision Trees Using Oracle Guides”; Proc. of the IEEE Symposium on Computational Intelligence and Data Mining; pp 238-244; 2009.

[9] U. Johansson; R. König; T. Löfström and L. Niklasson; ”Using Imaginary Ensembles to Select GP Classifiers”; EuroGP; 2010; In Press.

[10] L. I. Kuncheva and C. J. Whitaker; “Measures of Diversity in Classifier Ensem-bles and Their Relationship with the Ensemble Accuracy”; Machine Learning; (51):181-207; 2003.

[11] U. Johansson; T. Löfström and L. Niklasson; “The Importance of Diversity in Neural Network Ensembles - An Empirical Investigation”; Proc. of the International Joint Conference on Neural Networks; pp 661-666; 2007.

[12] U. Johansson; T. Löfström and U. Norinder; “Evaluating Ensembles on QSAR Classification”; Proc. of Skövde Workshop on Information Fusion Topics; pp 59-62; 2009.

[13] Z.-H. Zhou; J.-X. Wu and W. Tang. “Ensembling Neural Networks: Many Could Be Better Than All”; Artificial Intelligence; Vol. 137; No. 1-2:239-263; 2002.

[14] M. M. Islam; X. Yao; S. M. Shahriar Nirjon; M. A. Islam and K. Murase; “Bagging and boosting negatively correlated neural networks”. IEEE transactions on systems; man; and cybernetics; Part B: Cybernetics; 38(3):771-84; 2008.

[15] Y. Tsuruoka; J. McNaught; S. Ananiadou; “Normalizing biomedical terms by minimizing ambiguity and variability“; BMC Bioinformatics; Vol. 9; No. Suppl 3; 2008.

[16] E. Dura; O. Engkvist and S. Muresan; “Names of chemical compounds within drug discovery context“; Proc. of the 3rd Skövde Workshop on Information Fusion Topics; pp 55-58; 2009.

[17] K. M. Hettne; R. H. Stierum; M. J. Schuemie; P. J. M. Hendriksen; B. J. A. Schijvenaars; E. M. van Mulligen; J. Kleinjans; and J. A. Kors; “A dictionary to identify small molecules and drugs in free text“; Bioinformatics; September 16; 2009.

Citeringar i Crossref