Abstract
This study deals on estimation of ligand activity with its descriptors. So, to achieve this goal, two different approaches were implemented. In the first one, the intervals between samples were determined. But in the second method, the intervals were clustered with k-means method. Afterwards, best descriptors of each ligands were extracted with genetic algorithm. Then, observations were classified with One-Against-All method. Finally, the activity of each ligands were estimated by forty percent of samples. In the first method, AUC values were between fifty four to ninety seven percent. For second approaches, there were about ninety seven percent.
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Ghasemi, F. et al. (2015). Improving Activity Prediction of Adenosine A2B Receptor Antagonists by Nonlinear Models. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2015. Lecture Notes in Computer Science(), vol 9044. Springer, Cham. https://doi.org/10.1007/978-3-319-16480-9_61
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DOI: https://doi.org/10.1007/978-3-319-16480-9_61
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16479-3
Online ISBN: 978-3-319-16480-9
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