Support vector machines for drug discovery.
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Cites background or methods from "Support vector machines for drug di..."
...An optimal hyperplane attained by maximizing margin between classes in N-dimensional space (N is the number of features); it is denoted by a hyperplane, which is used to classify data points by setting decision boundaries [51]....
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...For drug-target interaction, it is specifically designed for integrating ligands and proteins of interest information as an essential component for SVM modeling [51]....
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Cites background or methods from "Support vector machines for drug di..."
...[29] K....
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...More detailed information can be found in Heikamp and Bajorath [29]....
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...Over a decade, various ML methods have been applied to biology, chemistry and drug discovery [29]....
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References
40,147 citations
"Support vector machines for drug di..." refers background in this paper
...The balance between these two objectives is important for the generalization performance of SVM models to predict new, previously unseen data [9,10]....
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11,211 citations
"Support vector machines for drug di..." refers methods in this paper
...In order to enable SVM model building in such cases, the socalled kernel trick [12] is applied that facilitates the derivation of a nonlinear decision function in the input space....
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10,696 citations