Interpretation of Nonlinear QSAR Models Applied to Ames Mutagenicity Data

By L. Carlsson, Ernst Ahlberg Helgee, S. Boyer. 2009

63 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Control and Systems Engineering

How researchers see this paper

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

1. Daniel Neagu
2. Jun Huan
3. Alexios Koutsoukas
4. Keith J. Monaghan
5. Jan Palczewski
6. Xiaoli Li
7. Anna Palczewska
8. Richard Marchese Robinson

Citations in the corpus

• Interpreting random forest classification models using a feature contribution method (2013, methodology): “In [8], the authors present a method for a local interpretation of Support Vector Machine (SVM) and Random Forest model by retrieving the variable corresponding to the largest component of the decisionfunction gradient at any point in the model.”
• Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data (2017, methodology): “Several approaches in the literature have been proposed for interpreting local and global QSAR models based on support vector machine and random forest techniques [46, 47].”
• Machine learning methods in chemoinformatics (2014, unknown): “110,111”