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The combining of the two secondary datasets was conducted in MS Excel as spreadsheet model.
Quality control of the metabolomics datasets was conducted in two steps.
A BLAST search of A. americanum ESTs against a compilation of 12 arthropod peptide datasets was conducted.
In 2012, a meta-analysis of summary results, combining the evidence from the Japanese and IEC GWAS datasets, was conducted.
Statistical testing of the various datasets was conducted using IBM SPSS Statistics ver. 20 (IBM Australia, Sydney, NSW, Australia).
First, the classification of the benchmark datasets was conducted to compare four cases: nothing (no-oversampling), AdaboostM1 [ 25], SMOTE, and the proposed over-sampling method (LVQ-SMOTE).
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To evaluate our proposed descriptor, a series of simulated experiments using synthetic datasets is conducted via shape matching tests.
Extensive experiments on real datasets are conducted to compare LWMF with state-of-the-art WMF algorithm.
To validate the proposed algorithm, multiple experiments, including the ISPRS benchmark datasets and ten forest datasets, are conducted and compared with several existing lidar filtering algorithms.
Extensive experiments over real-world datasets are conducted to demonstrate that the proposed protocols maintain high usability and scale well to large datasets.
Experiments involving four synthetic and two real datasets are conducted to show the performance of the introduced clustering algorithm in terms of cluster purity and normalized mutual information.
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