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Products > RandomForests > Product Overview > RandomForests Bibliography
RandomForests Bibliography

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Alvarez, S. Diaz-Uriarte, R. Osorio, A. Barroso, A. Melchor, L. Paz, M.F. Honrado, E. Rodriguez, R. Urioste, M. Valle, L. Diez, O. Cigudosa, J.C. Dopazo, J. Esteller, M. & Benitez, J.  (2005).  A predictor based on the somatec genomic changes of the BRCA1/BRCA2 tumors with BRCA1 promoter hypemethylation. . Clinical Cancer Research

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Breiman, L.  (2001).  Random Forest.  Machine Learning

Buckinx, W. & Van den Poel, D.  (2005).  Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting.  European Journal of Operational Research

Bureau, A. Dupuis, J. Falls, K. Lunetta, K.L. Hayward, B. Keith, T.P& Van Eerdeweigh.  (2005).  Identifying SNPs predictive of phenotype using random forests.  Genetic Epidemiology

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Cummings, M.P. & Segal, M.R.  (2004).  Few amino acid positions in rpoB are associated with most of the rifampin resistance in Mycobacterium. . BMC Bioinformatics

Cummings, M.P. & Myers, D.S.  (2004).  Simple statistical models predict C-to-U edited sites in plant mitochondrial RNA.  BMC Bioinformatics

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de la Calleja, J. & Fuentes, O.  (2004).  Automated classification of galaxy images.  Knowledge-Based Intelligent Information and Engineering Systems

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Goh, C.S. Lan, N. Douglas, S.M. Wu, B.L. Echols, N. Smith, A. Milburn, D. Montelione, G.T. Zhao, H.Y. & Gerstein, M.  (2004).  Mining the structural genomics pipeline: Identification of protein properties that affect high-throughput experimental analysis.  Journal of Molecular Biology

Guha, R. & Jurs, P.C.  (2004).  Development of linear, ensemble, and nonlinear models for the prediction and interpretation of the biological activity of a set of PDGFR inhibitors.  Journal of Chemical Information and Computer Sciences

Gunther, E.C Stone, D.J Gerwien, R.W Bento, P. & Heyes, M.P.&npsb; (2003). . Prediction of clinical drug efficacy by classification of drug induced genomic expression profiles in vitro.  Proceedings of the National Academy of Sciences of the United States of America

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Izmirlian, G.  (2004).  Application of the random forest classification algorithm to a SELDI-T OF proteomics study in the setting of a cancer prevention trial.  Annals of the New York Academy of Sciences

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Lunetta, K.L. Hayward, L.B. Segal, J. & Van Eerdewegh, P.  (2004).  Screening large-scale association study data: exploiting interactions using random forests.  BMC Genetics

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Royston, P.   (2003).  The use of random forests regression for estimating prognosis with survival data  Controlled Clinical Trials

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Shi, T. Seligson, D. Belldegrun, A.S. Palotie, A.& Horvath, S.  (2005).  Tumor classification by tissue microarray profiling: random forest clustering applied to renal cell carcinoma.  Modern Pathology

Svetnik, V. Liaw, A. Tong, C. & Wang, T.  (2004).  Application of Breiman's random forest to modeling structure-activity relationships of pharmaceutical molecules.  Multiple Classifier Systems, Proceedings

Svetnik, V. Liaw, A. Tong, C. Culberson, J.C. Sheridan, R.P.& Feuston, B.P.   (2003).  Random forest: A classification and regression tool for compound classification and QSAR modeling.  Journal of Chemical Information and Computer Sciences

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Vens, C. Van Assche, A. Blockeel, H. & Dzeroski, S.  (2004).  First order random forests with complex aggregates.  Inductive Logic Programming, Proceedings

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Wei, G. Cosman, P. Berry, C.C. Feng, Z.Y. & Schafer, W.R.  (2004). . Automatic tracking, feature extraction and classification of C-elegans phenotypes.  IEEE Transactions on Biomedical Enineering

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