Abstract
The classification of amino acids and their sequence analysis plays a vital role in life sciences and is a challenging task. Deep learning models have well-established frameworks for solving a broad spectrum of complex learning problems compared to traditional machine learning techniques. This article uses and compares state-of-the-art deep learning models like Convolution Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) to solve macromolecule classification problems using amino acid sequences. The CNN extracts features from amino acid sequences, which are treated as vectors with the use of word embedding. These vectors are fed to the above-mentioned models to train robust classifiers. The results show that word2vec as embedding combined with VGG-16 performs better than LSTM and GRU. The proposed approach gets an error rate of 1.5%.
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