Analysis of Compounds Activity Concept Learned by SVM Using Robust Jaccard Based Low-dimensional Embedding

• Authors: Stanisław Jastrzębski , Wojciech Marian Czarnecki
• Languages of publication: PL

Abstracts

PL

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.

Keywords

PL

Support Vector Machines, Locally Sensitive Hashing, Jaccard similarity

• Publisher: [unknown2]
• Journal: Schedae Informaticae
• Year: 2015
• Volume: 24

Dates

published

2015

online

06 - 07 - 2016

Contributors

author

Stanisław Jastrzębski

author

Wojciech Marian Czarnecki