Abstract
We formulate the task of predicting protein-disease associations as a multi-label classification task. We apply both problem transformation (binary relevance), i.e., local approaches, and algorithm adaptation methods (predictive clustering trees), i.e., global approaches. In both cases, methods for learning individual trees and tree ensembles (random forests) are used. We compare the predictive performance of the local and global approaches on one hand and different feature sets used to represent the proteins on the other.
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Acknowledgements
We acknowledge the support of the Slovenian Research Agency (grants P2-0103 and N2-0128), the European Commission (grant HBP, The Human Brain Project SGA2), and the ERDF (Interreg Slovenia-Italy project TRAIN). The computational experiments were executed on the computing infrastructure of the Slovenian Grid (SLING) initiative.
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Breskvar, M., Džeroski, S. (2020). Predicting Associations Between Proteins and Multiple Diseases. In: Helic, D., Leitner, G., Stettinger, M., Felfernig, A., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2020. Lecture Notes in Computer Science(), vol 12117. Springer, Cham. https://doi.org/10.1007/978-3-030-59491-6_36
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DOI: https://doi.org/10.1007/978-3-030-59491-6_36
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