  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.
