A large benchmark finds traditional imputation methods for scRNA-seq data generally outperform deep learning ones, but numerical recovery does not reliably improve biological downstream analyses and no method wins across all settings.
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New hardware-usage-based similarity metrics can identify matching computational kernels between proxy applications and performance suites on both CPU and GPU systems.
Hybrid genetic algorithm and region-growing segmentation for brain MRI that automatically selects initial points to reduce error.
citing papers explorer
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A Large-Scale Comparative Analysis of Imputation Methods for Single-Cell RNA Sequencing Data
A large benchmark finds traditional imputation methods for scRNA-seq data generally outperform deep learning ones, but numerical recovery does not reliably improve biological downstream analyses and no method wins across all settings.
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On Similarity of Computational Kernels in our Codes and Proxies
New hardware-usage-based similarity metrics can identify matching computational kernels between proxy applications and performance suites on both CPU and GPU systems.
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Improving Brain Magnetic Resonance Image MRI Segmentation via a Novel Algorithm based on Genetic and Regional Growth
Hybrid genetic algorithm and region-growing segmentation for brain MRI that automatically selects initial points to reduce error.