Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
Impact in
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- Journal of Chemical Information and Computer Sciences
In The Last Decade
doi.org/10.1021/ci034160g →Countries where authors are citing Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
This map shows the geographic impact of Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling more than expected).
Fields of papers citing Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
This network shows the impact of Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling.
About Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
This paper, published in 2003, received 2.7k indexed citations . Written by Vladimir Svetnik, Andy Liaw, Christopher Tong, Joseph Culberson, Robert P. Sheridan and Bradley P. Feuston covering the research area of Molecular Biology and Computational Theory and Mathematics. It is primarily cited by scholars working on Computational Theory and Mathematics (710 citations), Molecular Biology (606 citations), Artificial Intelligence (342 citations), Materials Chemistry (319 citations) and Environmental Engineering (233 citations). Published in Journal of Chemical Information and Computer Sciences.
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This paper is also available at doi.org/10.1021/ci034160g.