Abstract
The prediction of receptor-ligand pairs is an active area of biomedical and computational research. Oddly, the application of machine learning techniques to this problem is a relatively under-exploited approach. Here we seek to understand how the application of least squares support vector machines (LS-SVM) to this problem can improve receptor-ligand predictions. Over the past decade, the amount of protein-protein interaction (PPI) data available has exploded into a plethora of various databases derived from various wet-lab techniques. Here we use PPI data to predict receptor ligand pairings using LS-SVM. Our results suggest that this approach provides a meaningful prioritization of the receptor-ligand pairs.
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Iacucci, E., Moreau, Y. (2010). Towards Better Receptor-Ligand Prioritization: How Machine Learning on Protein-Protein Interaction Data Can Provide Insight Into Receptor-Ligand Pairs. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15819-3_35
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DOI: https://doi.org/10.1007/978-3-642-15819-3_35
Publisher Name: Springer, Berlin, Heidelberg
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