A new method for binary classification of proteins with Machine Learning
This work addresses protein classification for bioinformatics, but it is incremental as it applies existing methods to a specific domain.
The authors tackled protein structure classification by training deep learning models on images derived from the Protein Data Bank, using pre-trained CNNs like InceptionResNetV2 and InceptionV3 to extract features and classify molecules, with a comparative analysis of network performances.
In this work we set out to find a method to classify protein structures using a Deep Learning methodology. Our Artificial Intelligence has been trained to recognize complex biomolecule structures extrapolated from the Protein Data Bank (PDB) database and reprocessed as images; for this purpose various tests have been conducted with pre-trained Convolutional Neural Networks, such as InceptionResNetV2 or InceptionV3, in order to extract significant features from these images and correctly classify the molecule. A comparative analysis of the performances of the various networks will therefore be produced.