Supporting Information for the paper:
"Detection of a SARS-CoV-2 Sequence with Genosensors Using Data Analysis Based on Information Visualization and Machine Learning Techniques"
This repositoty contains the Machine Learning data computed from SEM images of the genosensors. Matlab files under /COVID-DNA_descriptors/ are organized as:
scale_method.mat
where "scale" are either 1 or 10, corresponding to 1,000× or 10,000×. The "method" is the computer vision algorithm used to extract visual descriptors from each image. The file contains a list of variable as follows:
charc -> cell array of experimental metadata of each sample
classes_8 -> array of sample classes
classes_9 -> array of sample classes
classes_binary -> array of sample classes in the binary form (positive and negative)
features -> m × n matrix where m corresponds to samples and n to features
filenames -> cell array with the file name of each sample
indexes_charc ->
method -> name of the method used for computing features
The original SEM images can be downloaded at: http://scg-turing.ifsc.usp.br/data/bases/COVID-DNA_SEM-images.zip
If you use this data, please cite our paper:
Soares, J. C., Soares, A. C., da Cruz Rodrigues, V., Oiticica, P. R. A., Raymundo-Pereira, P. A., Bott-Neto, J. L., ... & Oliveira Jr, O. N. (2021). Detection of a SARS-CoV-2 sequence with genosensors using data analysis based on information visualization and machine learning techniques. Materials Chemistry Frontiers.
@article{soares2021detection,
title={Detection of a SARS-CoV-2 sequence with genosensors using data analysis based on information visualization and machine learning techniques},
author={Soares, Juliana Coatrini and Soares, Andrey Coatrini and da Cruz Rodrigues, Valquiria and Oiticica, Pedro Ramon Almeida and Raymundo-Pereira, Paulo Augusto and Bott-Neto, Jose Luiz and Buscaglia, Lorenzo Antonio and Castro, Lucas and Ribas, Lucas C and Scabini, Leonardo and others},
journal={Materials Chemistry Frontiers},
year={2021},
publisher={Royal Society of Chemistry}
}