Analysis of Compounds Activity Concept Learned by SVM Using Robust Jaccard Based Low-dimensional Embedding
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RIS BIB ENDNOTEAnalysis of Compounds Activity Concept Learned by SVM Using Robust Jaccard Based Low-dimensional Embedding
Publication date: 11.04.2016
Schedae Informaticae, 2015, Volume 24, pp. 9 - 19
https://doi.org/10.4467/20838476SI.15.001.3023Authors
Analysis of Compounds Activity Concept Learned by SVM Using Robust Jaccard Based Low-dimensional Embedding
Support Vector Machines (SVM) with RBF kernel is one of the most successful models in machine learning based compounds biological activity prediction. Unfortunately, existing datasets are highly skewed and hard to analyze. During our research we try to answer the question how deep is activity concept modeled by SVM. We perform analysis using a model which embeds compounds’ representations in a low-dimensional real space using near neighbour search with Jaccard similarity. As a result we show that concepts learned by SVM is not much more complex than slightly richer nearest neighbours search. As an additional result, we propose a classification technique, based on Locally Sensitive ashing approximating the Jaccard similarity through minhashing technique, which performs well on 80 tested datasets (consisting of 10 proteins with 8 different representations) while in the same time allows fast classification and efficient online training.
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Information: Schedae Informaticae, 2015, Volume 24, pp. 9 - 19
Article type: Original article
Titles:
Analysis of Compounds Activity Concept Learned by SVM Using Robust Jaccard Based Low-dimensional Embedding
Analysis of Compounds Activity Concept Learned by SVM Using Robust Jaccard Based Low-dimensional Embedding
Faculty of Mathematics and Computer Science, Jagiellonian University, Krakow, Poland
Department of Mathematics Faculty of Mathematics and Computer Science Jagiellonian University, ul. Łojasiewicza 6, 30-348 Kraków, Poland
Published at: 11.04.2016
Article status: Open
Licence: None
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