KJCKorea
Journal Central

@article{ART002869933},
author={Jeung Min Lee and Hyun Lee},
title={Small CNN-RNN Engraft Model Study for Sequence Pattern Extraction in Protein Function Prediction Problems},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2022},
volume={27},
number={8},
pages={49-59},
doi={10.9708/jksci.2022.27.08.049}

TY - JOUR
AU - Jeung Min Lee
AU - Hyun Lee
TI - Small CNN-RNN Engraft Model Study for Sequence Pattern Extraction in Protein Function Prediction Problems
JO - Journal of The Korea Society of Computer and Information
PY - 2022
VL - 27
IS - 8
PB - The Korean Society Of Computer And Information
SP - 49
EP - 59
SN - 1598-849X
AB - In this paper, we designed a new enzyme function prediction model PSCREM based on a study that compared and evaluated CNN and LSTM/GRU models, which are the most widely used deep learning models in the field of predicting functions and structures using protein sequences in 2020, under the same conditions. Sequence evolution information was used to preserve detailed patterns which would miss in CNN convolution, and the relationship information between amino acids with functional significance was extracted through overlapping RNNs. It was referenced to feature map production. The RNN family of algorithms used in small CNN-RNN models are LSTM algorithms and GRU algorithms, which are usually stacked two to three times over 100 units, but in this paper, small RNNs consisting of 10 and 20 units are overlapped. The model used the PSSM profile, which is transformed from protein sequence data. The experiment proved 86.4% the performance for the problem of predicting the main classes of enzyme number, and it was confirmed that the performance was 84.4% accurate up to the sub-sub classes of enzyme number. Thus, PSCREM better identifies unique patterns related to protein function through overlapped RNN, and Overlapped RNN is proposed as a novel methodology for protein function and structure prediction extraction.
KW - PSSM;Deep learning;Protein Function Prediction;Feature Engraft Model;Overlapped R
DO - 10.9708/jksci.2022.27.08.049
ER -

Jeung Min Lee and Hyun Lee. (2022). Small CNN-RNN Engraft Model Study for Sequence Pattern Extraction in Protein Function Prediction Problems. Journal of The Korea Society of Computer and Information, 27(8), 49-59.

Jeung Min Lee and Hyun Lee. 2022, "Small CNN-RNN Engraft Model Study for Sequence Pattern Extraction in Protein Function Prediction Problems", Journal of The Korea Society of Computer and Information, vol.27, no.8 pp.49-59. Available from: doi:10.9708/jksci.2022.27.08.049

Jeung Min Lee, Hyun Lee "Small CNN-RNN Engraft Model Study for Sequence Pattern Extraction in Protein Function Prediction Problems" Journal of The Korea Society of Computer and Information 27.8 pp.49-59 (2022) : 49.

Jeung Min Lee, Hyun Lee. Small CNN-RNN Engraft Model Study for Sequence Pattern Extraction in Protein Function Prediction Problems. 2022; 27(8), 49-59. Available from: doi:10.9708/jksci.2022.27.08.049

Jeung Min Lee and Hyun Lee. "Small CNN-RNN Engraft Model Study for Sequence Pattern Extraction in Protein Function Prediction Problems" Journal of The Korea Society of Computer and Information 27, no.8 (2022) : 49-59.doi: 10.9708/jksci.2022.27.08.049

Jeung Min Lee; Hyun Lee. Small CNN-RNN Engraft Model Study for Sequence Pattern Extraction in Protein Function Prediction Problems. Journal of The Korea Society of Computer and Information, 27(8), 49-59. doi: 10.9708/jksci.2022.27.08.049

Jeung Min Lee; Hyun Lee. Small CNN-RNN Engraft Model Study for Sequence Pattern Extraction in Protein Function Prediction Problems. Journal of The Korea Society of Computer and Information. 2022; 27(8) 49-59. doi: 10.9708/jksci.2022.27.08.049

Jeung Min Lee, Hyun Lee. Small CNN-RNN Engraft Model Study for Sequence Pattern Extraction in Protein Function Prediction Problems. 2022; 27(8), 49-59. Available from: doi:10.9708/jksci.2022.27.08.049

Jeung Min Lee and Hyun Lee. "Small CNN-RNN Engraft Model Study for Sequence Pattern Extraction in Protein Function Prediction Problems" Journal of The Korea Society of Computer and Information 27, no.8 (2022) : 49-59.doi: 10.9708/jksci.2022.27.08.049