QSAR Models Using a Large Diverse Set of Estrogens

By Leming Shi, H. Fang, W. Tong, Jie Wu, Roger Perkins, R. Blair, W. Branham, Stacey L. Dial, C. Moland, D. Sheehan. Journal of chemical information and computer sciences, 2001

330 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Genetics, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/7cda4a467437892bc51a213541ac68ab15aaf7b6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Roberto Todeschini
2. Viviana Consonni
3. Davide Ballabio
4. Taku Kudo
5. Sebastian Nowozin
6. Ivan Gutman
7. Bo Zhou
8. Boris Furtula

In Genetics

1. Sebastian Nowozin
2. Koji Tsuda

In Health, Toxicology and Mutagenesis

1. Roberto Todeschini

Citations in the corpus

• Evaluation of model predictive ability by external validation techniques (2010, background): “Two different expressions for the calculation of external Q(2), that is, Q(2) based on predictions for external test objects, were found in the literature [14,16]:”
• gBoost: a mathematical programming approach to graph classification and regression (2009, methodology): “The best results are highlighted in bold fonts Measure CoMFA (Hong et al. 2003 MGK freqSVM gBoost Shi et al. 2001) EDKB-AR MAE – 0.229 0.193 0.183 Q 2 0.571 0.346 0.465 0.621 EDKB-ER MAE – 0.320 0.268 0.263 Q 2 0.660 0.267 0.532 0.541 EDKB-ES MAE – 0.322 0.248 0.216 Q 2 – 0.522 0.588”