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The iterative UFIR estimator commonly outperforms the OFIR one even if N opt is not large, and it is able to outperform the Kalman filter under real‐world operating conditions and when the noise statistics are not known exactly.
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The pathway based gene expression similarity measuring method outperforms commonly used similarity measuring methods.
For the realistic systems biology models tested, the new integrator outperforms commonly used state of the art integrators when parameter sensitivities are required.
When applied to simulated data, it outperforms commonly used clustering methods, discovers overlapping clusters of diverse sizes and is robust in the presence of noise.
When applied to simulated data it outperforms commonly used clustering and bi-clustering approaches and iteratively discovers gene set modules made up of both strong and weaker signals.
Numbers crunchers on Kaggle have bested years of academic research by predicting how long people with HIV would live, outperformed commonly used algorithms for mapping dark matter and discovered that people who order vegetarian meals from airlines are more likely to show up for the flight.
Several nonparametric estimators outperform commonly used treatment estimators based on parametric propensity scores in terms of root mean squared error (RMSE), even though average RMSEs based on the 16 simulation designs considered are not statistically significantly different across the estimators investigated.
Moreover, the method outperformed commonly used methods with higher robustness to noise on all simulated rs-fMRI datasets.
Validation RT-qPCR experiments across several organisms showed that the candidates proposed by RefGenes generally outperformed commonly used reference genes.
This experiment proved that liver-specific stable genes, as identified from Affymetrix microarray data from liver samples, outperformed commonly used reference genes for the normalization of RT-qPCR data from liver.
Specifically, we demonstrate that it outperforms the commonly used weighted-sum approach.
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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