Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Molecular feature mining in HIV data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Text classification using string kernels
The Journal of Machine Learning Research
An introduction to variable and feature selection
The Journal of Machine Learning Research
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
State of the art of graph-based data mining
ACM SIGKDD Explorations Newsletter
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Extensions of marginalized graph kernels
ICML '04 Proceedings of the twenty-first international conference on Machine learning
The predictive toxicology evaluation challenge
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Classes of Kernels for Hit Definition in Compound Screening
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
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Kernel methods are a class of algorithms for pattern analysis with a number of convenient features. This paper proposes extension of the kernel method for biological screening data including chemical compounds. Our investigation of extending kernel aims to combine properties of graphical structure and molecule descriptors. The use of such kernels allows comparison of compounds, not only on graphs but also on important molecular descriptors. Our experimental evaluation of eight different classification problems shows that a proposed special kernel, which takes into account chemical molecule structure and molecule descriptors, statistically improves significantly the classification performance.