A novel hybrid CNN and BiGRU-Attention based deep learning model for protein function prediction

Sharma Lavkush (), Deepak Akshay (), Ranjan Ashish () and Krishnasamy Gopalakrishnan ()
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Sharma Lavkush: Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, Bihar, India
Deepak Akshay: Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, Bihar, India
Ranjan Ashish: Department of Computer Science and Engineering, ITER, Siksha ‘O’ Anusandhan University (Deemed to be University), Bhubaneswar, Odisha, India
Krishnasamy Gopalakrishnan: Department of Mathematics and Computer Science, Central State University, Wilberforce, USA

Statistical Applications in Genetics and Molecular Biology, 2023, vol. 22, issue 1, 18

Abstract: Proteins are the building blocks of all living things. Protein function must be ascertained if the molecular mechanism of life is to be understood. While CNN is good at capturing short-term relationships, GRU and LSTM can capture long-term dependencies. A hybrid approach that combines the complementary benefits of these deep-learning models motivates our work. Protein Language models, which use attention networks to gather meaningful data and build representations for proteins, have seen tremendous success in recent years processing the protein sequences. In this paper, we propose a hybrid CNN + BiGRU – Attention based model with protein language model embedding that effectively combines the output of CNN with the output of BiGRU-Attention for predicting protein functions. We evaluated the performance of our proposed hybrid model on human and yeast datasets. The proposed hybrid model improves the Fmax value over the state-of-the-art model SDN2GO for the cellular component prediction task by 1.9 %, for the molecular function prediction task by 3.8 % and for the biological process prediction task by 0.6 % for human dataset and for yeast dataset the cellular component prediction task by 2.4 %, for the molecular function prediction task by 5.2 % and for the biological process prediction task by 1.2 %.

Keywords: attention technique; CNN; gated recurrent unit; protein language models; protein sequence (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1515/sagmb-2022-0057

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