Add to ORKGIn this presentation, we give an introduction to graph mining and an overview of its applications in chemoinformatics for Quantitative Structure-Activity Relationships.Invited talkstatus: publishe
In drug design and development, one of the most important things is how to find the candidate chemic...
© The Author(s) 2019. The goal of quantitative structure activity relationship (QSAR) learning is to...
This paper analyses the graph mining problem, and the frequent pattern mining task associated with i...
We propose a new boosting method that systematically combines graph mining and mathematical programm...
Graph mining is the study of how to perform data mining and machine learning on data represented wit...
Small molecules in chemistry can be represented as graphs. In a quantitative structure-activity rela...
This talk showcases how to mine knowledge graph for drug discovery. It discusses the cutting-edge ma...
There are several approaches in trying to solve the Quantitative 1Structure-Activity Relationship (Q...
It is increasingly clear that machine learning algorithms need to be integrated in an iterative scie...
During the last decade non-linear machine-learning methods have gained popularity among QSAR modeler...
Quantitative structure-activity relationship (QSAR) models play a key role in lead optimization, whe...
International audienceChemoinformatics is a well established research field concerned with the disco...
In many real-world problems, one deals with input or output data that are structured. This thesis in...
Summary: Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory ...
Abstract It is increasingly clear that machine learning algorithms need to be inte-grated in an iter...
In drug design and development, one of the most important things is how to find the candidate chemic...
© The Author(s) 2019. The goal of quantitative structure activity relationship (QSAR) learning is to...
This paper analyses the graph mining problem, and the frequent pattern mining task associated with i...
We propose a new boosting method that systematically combines graph mining and mathematical programm...
Graph mining is the study of how to perform data mining and machine learning on data represented wit...
Small molecules in chemistry can be represented as graphs. In a quantitative structure-activity rela...
This talk showcases how to mine knowledge graph for drug discovery. It discusses the cutting-edge ma...
There are several approaches in trying to solve the Quantitative 1Structure-Activity Relationship (Q...
It is increasingly clear that machine learning algorithms need to be integrated in an iterative scie...
During the last decade non-linear machine-learning methods have gained popularity among QSAR modeler...
Quantitative structure-activity relationship (QSAR) models play a key role in lead optimization, whe...
International audienceChemoinformatics is a well established research field concerned with the disco...
In many real-world problems, one deals with input or output data that are structured. This thesis in...
Summary: Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory ...
Abstract It is increasingly clear that machine learning algorithms need to be inte-grated in an iter...
In drug design and development, one of the most important things is how to find the candidate chemic...
© The Author(s) 2019. The goal of quantitative structure activity relationship (QSAR) learning is to...
This paper analyses the graph mining problem, and the frequent pattern mining task associated with i...