A performance comparison of modern statistical techniques for molecular descriptor selection and retention prediction in chromatographic QSRR studies

By Timothy Hancock, R. Put, D. Coomans, Y. Vander Heyden, Y. Everingham. 2005

124 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computational Theory and Mathematics, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/4e7637df3fb83d244a7e581c04b91fa4eae2dc5b

How researchers see this paper

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

In Analytical Chemistry

1. Antanas Verikas
2. Marija Bacauskiene
3. Adas Gelzinis

In Computational Theory and Mathematics

1. Chao Huang
2. Antanas Verikas
3. Marija Bacauskiene
4. Yang Ling
5. Yonghua Wang
6. Wuxia Zhang
7. Adas Gelzinis
8. Xue Xu

Citations in the corpus

• A Novel Chemometric Method for the Prediction of Human Oral Bioavailability (2012, methodology): “Dragon descriptors have been successfully used for quantitatively representing the structural and physicochemical features of a molecule [22–24].”
• Mining data with random forests: A survey and results of new tests (2011, methodology): “MLR with 35 variables o RF with 35 variables Predicting the chromatographic retention of basic drugs (RF, CART, TreeBoost, PLS, GA-MLR) [80] Data: a regression task, a set of 83 drugs, 1272 variables (molecular descriptors).”