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Our classifier was capable of achieving a well prediction performance with an accuracy of 95.6% may be due to the using of a combined feature vector, because additional seven features used in our study have been proved to be one part of the optimized features subset in pre-miRNAs prediction by Wang et al. [ 3].
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The resulting models were studied with regard to the effect of varying the training set size both with regards to training time as well as prediction performance, and the models were made available from the Bioclipse platform [15, 16] via its decision support functionality [17].
The three prediction models showed general applicability and a reasonably well-predictive performance, especially in comparison to FIGO stage.
Section 2 defines the problem of criminal incident modeling formally, describes ST-GAM and LST-GAM in detail, and discusses how to estimate parameters as well as evaluate spatial prediction performance.
We perform support vector regression, sequential minimal optimization regression and multilayer perceptron algorithms with linear and poly kernels on experimental dataset for appropriate model selecting, and multilayer perceptron model works well for its distinguished prediction performance with high correlation coefficient and low error values.
Figure 3 shows the 10-fold cross-validation as well as cross-species prediction performance of our algorithm vis-à-vis mirSVR [ 26], PITA [ 32], TargetScan [ 27, 33], and MIRZA [ 28], on the human and mouse datasets.
These control the quality of a reservoir model with effect on wells production behavior and prediction performance.
Prediction performance as well as true interactions may be specific to certain experimental settings.
The methodology has some nice features including timely detection of outbreaks, robust inference to model misspecification, reasonable prediction performance as well as attractive analytical and visualization tool to assist public health authorities in risk assessment.
Fifth, a series of subjective experiments are conducted to calibrate the parameters in the SDF model as well as to test the quality prediction performance of objective models on subjective MOS.
We evaluated GAP by comparing its prediction performance with several other well-known functional interaction prediction tools over a comprehensive dataset of known direct and indirect interactions, and observed significantly better prediction performance.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com