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The authors determined the feature T(O...Br) to be the most significant descriptor with respect to the endpoint.
This SMARTS string furthermore showed to be the most significant descriptor in the decision tree approach from which guidelines for the design of safe compounds are suggested.
They successfully used a decision gradient function from RF or SVM models to identify the most significant descriptor for a prediction.
In the case of Fungi, the pure effect of plant communities was the most significant descriptor.
In this case, the α was the most significant descriptor.
The dipole moment μ was the most significant descriptor.
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The most significant descriptors in these models were related to monomer reactivity.
In this correction approach, PLS is used to select the most significant descriptors.
The most significant descriptors identified by decision tree and the experimental dataset were utilized to predict fibrinogen adsorption using an artificial neural network (ANN).
Herein, PLS is used to screen the molecular descriptors; that is, the method selects the most significant descriptors from all of the available descriptors according to the PLS fitting coefficients.
The loadings plot (Figure 5C) reveals that the buckets associated with taurine (δ = 3.20 3.50 ppm) are the most significant descriptors associated with this group separation.
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