iPro-TCN: Prediction of DNA Promoters Recognition and Their Strength Using Temporal Convolutional Network-Reference-Cited by-同舟云学术

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iPro-TCN: Prediction of DNA Promoters Recognition and Their Strength Using Temporal Convolutional Network

Published:2023 Issue: Volume:11 Page:66113-66121
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Raza Ali¹,Alam Waleed²^ORCID,Khan Shahzad¹,Tahir Muhammad³^ORCID,Chong Kil To²

Affiliation:

1. Physical and Numerical Sciences, Qurtuba University of Science and Information Technology, Peshawar, Pakistan

2. Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, South Korea

3. Department of Computer Science, Abdul Wali Khan University, Mardan, Pakistan

Funder

National Research Foundation of Korea (NRF) funded by the Korean Government [Ministry of Industry Science and Technology (MIST)]

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10147817.pdf?arnumber=10147817

Reference50 articles.

1. Sigma70Pred: A highly accurate method for predicting sigma70 promoter in Escherichia coli K-12 strains

2. iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition

3. iPSW(2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition

4. iProm-phage: A two-layer model to identify phage promoters and their types using a convolutional neural network

5. DNA duplex stability as discriminative characteristic for Escherichia coli σ54- and σ28- dependent promoter sequences

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