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Estimates of errors and uncertainty are often poorly defined and typically fail to represent the spatial heterogeneity of the dataset, which may introduce bias or error for many analyses.
Local models are based on chemical similarity amongst the dataset which may be structural or mechanistic.
Summary statistics only uses part of information in dataset, which may reduce the statistical power.
The authors of this paper have made their own dataset which may be used by researchers working on a problem of face recognition at a distance.
We used a retrospective dataset which may induce selection bias and missing data.
Hence, the classification model would be trained with a balance dataset, which may result in a high testing rate.
Similar(40)
HDFS provides the raw underlying storage on commodity hardware for massive datasets (which may be petabytes in size), and MapReduce provides the programming model to run computations over significant portions of the dataset in parallel.
In addition, there are several other problems in the use of datasets, which may cause further uncertainties because of a lack of detailed protocol for the handling of these.
To the best of our knowledge, little work has been reported from cheminformatics study for these especially pairwise datasets, which may provide insight into the mechanism of actions of the compounds and relationship between chemical structures and functions, as well as guidance for lead compound selection and optimization.
Here we propose a novel unsupervised approach for the integrative modelling of multiple datasets, which may be of different types.
However, these adjustments require the existence of reference datasets, which may be laborious or expensive to collect.
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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