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2017 | OriginalPaper | Buchkapitel

ALADIN: A New Approach for Drug–Target Interaction Prediction

verfasst von : Krisztian Buza, Ladislav Peska

Erschienen in: Machine Learning and Knowledge Discovery in Databases

Verlag: Springer International Publishing

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Abstract

Due to its pharmaceutical applications, one of the most prominent machine learning challenges in bioinformatics is the prediction of drug–target interactions. State-of-the-art approaches are based on various techniques, such as matrix factorization, restricted Boltzmann machines, network-based inference and bipartite local models (BLM). In this paper, we extend BLM by the incorporation of a hubness-aware regression technique coupled with an enhanced representation of drugs and targets in a multi-modal similarity space. Additionally, we propose to build a projection-based ensemble. Our

https://static-content.springer.com/image/chp%3A10.1007%2F978-3-319-71246-8_20/457129_1_En_20_IEq1_HTML.gif

technique (ALADIN) is evaluated on publicly available real-world drug–target interaction datasets. The results show that our approach statistically significantly outperforms BLM-NII, a recent version of BLM, as well as NetLapRLS and WNN-GIP.

Code related to this chapter is available at: https://github.com/lpeska/ALADIN

Data related to this chapter are available at: https://zenodo.org/record/556337#.WPiAzIVOIdV

Supplementary material is available at: http://www.biointelligence.hu/dti/

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Vorheriges Kapitel Adaptive Skip-Train Structured Regression for Temporal Networks

Nächstes Kapitel Co-Regularised Support Vector Regression

See http://www.biointelligence.hu/dti for further results.

The datasets are available at https://zenodo.org/record/556337#.WPiAzIVOIdV.

In our initial experiments, we observed that increasing the number of base models results in asymptotically increasing performance. For example, we obtained AUPR of 0.835, 0.867 and 0.871 with 5, 25 and 100 base models on the Ion Channel dataset. We made similar observations on the other datasets both in terms of AUC and AUPR. Therefore, using \(N=25\) base models seems to be a fair compromise between runtime and prediction quality.

\(\beta =\beta _{drug}=\beta _{target}\) and \(\gamma =\gamma _{drug}=\gamma _{target}\).

See https://github.com/lpeska/ALADIN for our codes.

https://sourceforge.net/projects/pyhubs/.

https://github.com/stephenliu0423/PyDTI.

These results are in accordance with our further observations: considering the input data of the local models, the skewness of the distribution of bad k-nearest neighbor occurrences (with \(k=3\)), which is often used to quantify the presence of bad hubs [33], is remarkably high, between 1.61 and 11.13.

http://www.kegg.jp/.

https://www.drugbank.ca/.

http://matador.embl.de/.

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Titel: ALADIN: A New Approach for Drug–Target Interaction Prediction
verfasst von: Krisztian Buza
Ladislav Peska
Verlag: Springer International Publishing
Buch: Machine Learning and Knowledge Discovery in Databases
Print ISBN: 978-3-319-71245-1

Electronic ISBN: 978-3-319-71246-8

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-71246-8_20

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