Large-Scale Learning of Structure-Activity Relationships Using a Linear Support Vector Machine and Problem-Specific Metrics
By Georg Hinselmann, Lars Rosenbaum, Andreas Jahn, Nikolas Fechner, Claude Ostermann, Andreas Zell. 2011
33 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Molecular Biology
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
- Sunghwan Kim
- Evan E. Bolton
- Stephen H. Bryant
- Lianyi Han
- Bo Yu
- Volker Hähnke
- Andreas Zell
- Georg Hinselmann
In Molecular Biology
- Sunghwan Kim
- Evan E. Bolton
- Stephen H. Bryant
- Bo Yu
- Lianyi Han
- Volker Hähnke
- Andreas Zell
- Georg Hinselmann
Citations in the corpus
- Interpreting linear support vector machine models with heat map molecule coloring (2011, background): “Linear SVMs in combination with sparse molecular fingerprints showed a convincing performance on several large-scale data sets [7].”
- Accurate and efficient target prediction using a potency-sensitive influence-relevance voter (2015, unknown): “Machine learning methods can produce good results in target prediction and other domains [54–56], but they are sometimes criticized as being “black-box” methods, and therefore difficult to interpret.”
- PubChem structure–activity relationship (SAR) clusters (2015, background): “Despite these difficulties, there has been an increasing interest in systematic large-scale mining of SARs from bioactivity data available in the public domain [33, 34].”