Efficient classification learning of biochemical structured data by means of relevance weighting for sensoric response features

Abstract

We present an approach for generating vectorial representations of graphs for machine learning applications based on a sensoric response principle and multiple graph kernels. The sensor perspective reduces the graph kernel computations significantly. Thus, multiple kernel (relevance) learning can be realized using the interpretable generalized matrix learning vector quantization (GMLVQ) classifier. Results obtained in small molecule classification serve as proof of concept. * K.S.B and M.K. are supported by a grant of the European Social Fund (ESF).

Tutorial - Machine Learning and Information Theoretic Methods for Molecular Biology and Medicine

“…Respective data fusion processes have to be applied to wrangling data into such a format that machine learning algorithms can deal with them adequately [43]. For example, graph kernels and graph embedding can reflect relations between complex data objects while at the same time they can be handled efficiently in an appropriate framework [51,15].…”