Leveraging chemical background knowledge for the prediction of growth inhibition

Proceedings - Sixth IEEE Symposium on BioInformatics and BioEngineering, BIBE 2006

Volumn , Issue , 2006, Pages 319-324

  Richter, Lothar ^a   Hechtl, Stefan ^a   Kramer, Stefan ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BIOACTIVITY; GROWTH KINETICS; KNOWLEDGE ACQUISITION; MOLECULAR BIOLOGY;

ANTI CANCER AGENTS (ACA); CELL LINE SCREENING DATA; CHEMICAL BACKGROUND KNOWLEDGE;

BIOINFORMATICS;

EID: 34547465076     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BIBE.2006.253296     Document Type: Conference Paper

References (11)

1. Deshpande M., Kuramochi M., Karypis G.: Frequent substructure-based approaches for classifying chemical compounds. In Proc. Third Int. Conf. on Data Mining (ICDM 2003), IEEE Computer Society Press, 2003.
2. Helma C., Cramer T., Kramer S., De Raedt L.: Data mining and machine learning techniques for the identification of mutagenicity inducing substructures and structure activity relationships of noncongeneric compounds. J. Chem. Inf. Comput. Sci., 2004, pp. 1402-1411.
3. Jahn K., Kramer S.: Optimizing gSpan for molecular datasets. In (Nijssen et al. eds.) Proc. Third Int. Workshop on Mining Graphs, Trees and Sequences (MGTS 2005), Porto, 2005.
4. Kramer S., De Raedt L., Helma C.: Molecular feature mining in HIV data. In Proc. Seventh ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD 2001), ACM Press, 2001.
5. Raymond J. W., Willett P.: Effectiveness of graph-based and fingerprint-based similarity measures for virtual screening of 2D chemical structure databases. J.Comp.-Aid. Mol. Des., 2002, pp. 59-71.
6. Richter L., Rückert U., Kramer S.: Learning a predictive model for growth inhibition from the NCI DTP human tumor cell line screening data: does gene expression make a difference? In (Altman R. et al. eds.) Pacific Symposium on Biocomputing 2006, World Scientific Publishing Co. Pte Ltd., Singapore, 2006, pp. 596-607.
7. Rückert U., Kramer S.: Frequent free tree discovery in graph data. In (Haddad H. et al. eds.) Proc. ACM Symp. on Appl. Comp. (SAC2004), ACM Press, 2004, pp. 564-570.
8. RuleQuest Research (2005). http://www.rulequest.com/cubist-info.html
9. Sommer S.: Lazy structure-activity relationships. Diploma Thesis, TU München, 2006.
10. Weinstein J.N., Kohn K.W., Grever M.R., Viswanadhan V.N., Rubinstein L.V., Monks A., Scudiero D.A., Welch L., Koutsoukos A.D., Chiausa A.J., Paull K.D.: Neural computing in cancer drug development: predicting mechanisms of action. Science, 1991, pp. 447-51.
11. van Osdol W.W., Myers T.G., Paull K.D., Kohn K.W., Weinstein J.N.: Use of the Kohonen self-organizing map to study the mechanisms of action of chemotherapeutic agents. J. Natl. Cancer Inst., 1994, pp. 1853-1859.