Add to ORKGMotivation: The development of in silico models to predict chemical carcinogenesis from molecular structure would help greatly to prevent environmentally caused cancers. The Predictive Toxicology Challenge (PTC) competition was organized to test the state-of-the-art in applying machine learning to form such predictive models
Abstract. The goal of predictive toxicology is the automatic construc-tion of carcinogenecity models...
Computational prediction of toxicity has reached new heights as a result of decades of growth in the...
Two approaches for the prediction of which of two vehicles will result in lower toxicity for antican...
Motivation: The development of in silico models to predict chemical carcinogenesis from molecular st...
Motivation The development of in silico models to pre-dict chemical carcinogenesis from molecular st...
Motivation: Chemical carcinogenicity is of primary inter-est, because it drives much of the current ...
This paper describes my submission to one of the sub-problems formulated for the Predictive Toxicolo...
\u3cp\u3eThe ability to computationally predict the effects of toxic compounds on humans could help ...
International audienceThe ability to computationally predict the effects of toxic compounds on human...
The ability to computationally predict the effects of toxic compounds on humans could help address t...
[[abstract]]Carcinogenicity is one of the most critical endpoints for the risk assessment of food co...
This paper discusses the “Leuven” submission (More specifically, the submission by the Machine Learn...
Until recently, problem solvers have typically used single-technique-based tools to build the soluti...
The increasing use of Machine Learning (ML) in the drug and food industry is undeniable and it is im...
Motivation: A model for learning potential causes of toxicity from positive and negative examples an...
Abstract. The goal of predictive toxicology is the automatic construc-tion of carcinogenecity models...
Computational prediction of toxicity has reached new heights as a result of decades of growth in the...
Two approaches for the prediction of which of two vehicles will result in lower toxicity for antican...
Motivation: The development of in silico models to predict chemical carcinogenesis from molecular st...
Motivation The development of in silico models to pre-dict chemical carcinogenesis from molecular st...
Motivation: Chemical carcinogenicity is of primary inter-est, because it drives much of the current ...
This paper describes my submission to one of the sub-problems formulated for the Predictive Toxicolo...
\u3cp\u3eThe ability to computationally predict the effects of toxic compounds on humans could help ...
International audienceThe ability to computationally predict the effects of toxic compounds on human...
The ability to computationally predict the effects of toxic compounds on humans could help address t...
[[abstract]]Carcinogenicity is one of the most critical endpoints for the risk assessment of food co...
This paper discusses the “Leuven” submission (More specifically, the submission by the Machine Learn...
Until recently, problem solvers have typically used single-technique-based tools to build the soluti...
The increasing use of Machine Learning (ML) in the drug and food industry is undeniable and it is im...
Motivation: A model for learning potential causes of toxicity from positive and negative examples an...
Abstract. The goal of predictive toxicology is the automatic construc-tion of carcinogenecity models...
Computational prediction of toxicity has reached new heights as a result of decades of growth in the...
Two approaches for the prediction of which of two vehicles will result in lower toxicity for antican...