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[HTML][HTML] Hybridizing feature selection and feature learning approaches in QSAR modeling for drug discovery

I Ponzoni, V Sebastián-Pérez, C Requena-Triguero… - Scientific reports, 2017 - nature.com
Quantitative structure–activity relationship modeling using machine learning techniques
constitutes a complex computational problem, where the identification of the most …
被引用次数：76 相关文章

Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery

I Ponzoni, V Sebastián Pérez, C Requena Triguero… - 2017 - lareferencia.info
Quantitative structure–activity relationship modeling using machine learning techniques
constitutes a complex computational problem, where the identification of the most …

Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery

I Ponzoni, V Sebastián-Pérez… - Scientific …, 2017 - ui.adsabs.harvard.edu
Quantitative structure-activity relationship modeling using machine learning techniques
constitutes a complex computational problem, where the identification of the most …

Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery

I Ponzoni, V Sebastián Pérez… - 2017 - datosdeinvestigacion.conicet.gov.ar
Quantitative structure–activity relationship modeling using machine learning techniques
constitutes a complex computational problem, where the identification of the most …

Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery

I Ponzoni, V Sebastián Pérez… - 2017 - notablesdelaciencia.conicet.gov.ar
Quantitative structure–activity relationship modeling using machine learning techniques
constitutes a complex computational problem, where the identification of the most …
[PDF] researchgate.net

[PDF][PDF] Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery

I Ponzoni, V Sebastián-Pérez, C Requena-Triguero… - researchgate.net
Results In this section, several QSAR models inferred by feature selection and feature
learning for different physicochemical properties are described. Figure 1 presents a scheme …

Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery.

I Ponzoni, V Sebastián-Pérez… - Scientific …, 2017 - europepmc.org
Quantitative structure–activity relationship modeling using machine learning techniques
constitutes a complex computational problem, where the identification of the most …
[PDF] core.ac.uk

[PDF][PDF] Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery

I Ponzoni, V Sebastián-Pérez, C Requena-Triguero… - core.ac.uk
Results In this section, several QSAR models inferred by feature selection and feature
learning for different physicochemical properties are described. Figure 1 presents a scheme …

Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery

I Ponzoni, V Sebastián-Pérez, C Requena-Triguero… - digital.csic.es
Results In this section, several QSAR models inferred by feature selection and feature
learning for different physicochemical properties are described. Figure 1 presents a scheme …
[HTML] nih.gov

[HTML][HTML] Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery

I Ponzoni, V Sebastián-Pérez… - Scientific …, 2017 - ncbi.nlm.nih.gov
Quantitative structure–activity relationship modeling using machine learning techniques
constitutes a complex computational problem, where the identification of the most …