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Though many phenotypes (more than 55%) were classified with accuracy above 80%, we used a 60% classification accuracy cutoff to accommodate noise in input data such as wrong gene calling or imbalance in phenotype data.
In the comparison of two strains of P. marinus, 4 of 6 clusters from each genome were classified with accuracy >90% and were always associated with the correct genome if not the correct cluster within that genome.
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After removing outliers from training samples, we were able to identify more predominant classifier genes by which independent validation samples were correctly classified with higher accuracy.
These pixels were classified with 100% accuracy using CDAF technique.
Six defect types from cold strips were correctly classified with 87%to94%4% accuracy.
The internal carotid arterial Doppler signals were classified with the accuracy varying from 94.87% to 97.44%.
The ophthalmic arterial Doppler signals were classified with the accuracy varying from 93.75% to 97.06%.
All forest LTs were well classified with an accuracy varying from 78.4% to 97.5%.
Applicability domain provides additional information to identify which compounds are classified with best accuracy and which are expected to have poor and unreliable predictions.
Results showed that the six tongue motions (i.e., five voluntary tongue motions and saliva swallowing) were classified with high accuracy of 95.1 ± 1.9%%.
Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were classified with the accuracy of 96.94% by the combined neural network.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com