Macromolecule Classification Based on the Amino-acid Sequence
This work addresses macromolecule classification for life sciences, but it is incremental as it applies existing deep learning methods to a specific domain.
The study tackled protein sequence classification using deep learning and NLP word embeddings, achieving nearly 99% accuracy on train and test sets for grouping sequences into DNA, RNA, Protein, and hybrid classes.
Deep learning is playing a vital role in every field which involves data. It has emerged as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems which were difficult to solve using traditional machine learning techniques in the past. In this study we focused on classification of protein sequences with deep learning techniques. The study of amino acid sequence is vital in life sciences. We used different word embedding techniques from Natural Language processing to represent the amino acid sequence as vectors. Our main goal was to classify sequences to four group of classes, that are DNA, RNA, Protein and hybrid. After several tests we have achieved almost 99% of train and test accuracy. We have experimented on CNN, LSTM, Bidirectional LSTM, and GRU.