One- to Four-Dimensional Kernels for Virtual Screening and the Prediction of Physical, Chemical, and Biological Properties

By Chloé-Agathe Azencott, Alex Ksikes, S. Joshua Swamidass, Jonathan H. Chen, Liva Ralaivola, Pierre Baldi. 2007

63 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computational Theory and Mathematics, Spectroscopy

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. Ron O. Dror
2. Stephan Eismann
3. Raphael J. L. Townshend
4. Brandon M. Anderson
5. Russ B. Altman
6. Isabelle M Guyon
7. Risi Kondor
8. Patricia Suriana

In Spectroscopy

1. Russ B. Altman
2. Raphael J. L. Townshend
3. Alexander Derry
4. Stephan Eismann
5. Ron O. Dror
6. Patricia Suriana

Citations in the corpus

• Analysis of the IJCNN 2007 agnostic learning vs. prior knowledge challenge (2008, background): “The winner in HIVA (Chloé-Agathe Azencott of the Pierre Baldi Laboratory at UCI) is a specialist in this kind of dataset, on which she is working towards her PhD [2].”
• ATOM3D: Tasks On Molecules in Three Dimensions (2020, background): “Three dimensional molecular data have long been pursued as an attractive source of information in molecular learning and chemoinformatics, but until recently have achieved underwhelming results relative to 1D and 2D representations (Swamidass et al., 2005; Azencott et al., 2007).”
• Agnostic Learning vs. Prior Knowledge Challenge (2007, unknown): “Her leading entry may use kernels inspired by her previous work [39].”