Macromolecule Classification Based on the Amino-acid Sequence

2020/01/06 by Faisal Ghaffar, Ghaffar, Faisal, Sarwar Shah Khan +5
Biochemistry, Genetics and Molecular Biology · #Biomolecules (q-bio.BM) #FOS: Biological sciences #FOS: Computer and information sciences #Fractal and DNA sequence analysis #Genomics and Phylogenetic Studies #Machine Learning (cs.LG) #Machine Learning in Bioinformatics

paper · pdf · doi:10.48550/arxiv.2001.01717

openalex publication_date 2020/01/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Gradient-based learning applied to document recognition
• Distributed Representations of Words and Phrases and their Compositionality
• ImageNet classification with deep convolutional neural networks

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