DNA-Binding Protein Prediction Based on Deep Learning Feature Fusion

2021 ◽  
pp. 627-633
Author(s):  
Shixuan Guan ◽  
Tengsheng Jiang ◽  
Weizhong Lu ◽  
Qiming Fu ◽  
Haiou Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dna Binding ◽  
Feature Fusion ◽  
Binding Protein ◽  
Dna Binding Protein ◽  
Protein Prediction

scholarly journals KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuran Jia ◽  
Shan Huang ◽  
Tianjiao Zhang
Keyword(s):  
Dna Binding ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Protein Identification ◽  
Feature Fusion ◽  
Binding Protein ◽  
Rapid Development ◽  
Dna Binding Protein ◽  
Feature Extraction Method ◽  
Independent Test Dataset

DNA-binding protein (DBP) is a protein with a special DNA binding domain that is associated with many important molecular biological mechanisms. Rapid development of computational methods has made it possible to predict DBP on a large scale; however, existing methods do not fully integrate DBP-related features, resulting in rough prediction results. In this article, we develop a DNA-binding protein identification method called KK-DBP. To improve prediction accuracy, we propose a feature extraction method that fuses multiple PSSM features. The experimental results show a prediction accuracy on the independent test dataset PDB186 of 81.22%, which is the highest of all existing methods.

DPP-PseAAC: A DNA-binding protein prediction model using Chou’s general PseAAC

2018 ◽  
Vol 452 ◽  
pp. 22-34 ◽  
Author(s):  
M. Saifur Rahman ◽  
Swakkhar Shatabda ◽  
Sanjay Saha ◽  
M. Kaykobad ◽  
M. Sohel Rahman
Keyword(s):  
Dna Binding ◽  
Prediction Model ◽  
Binding Protein ◽  
Dna Binding Protein ◽  
Protein Prediction

scholarly journals HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Rianon Zaman ◽  
Shahana Yasmin Chowdhury ◽  
Mahmood A. Rashid ◽  
Alok Sharma ◽  
Abdollah Dehzangi ◽  
...  
Keyword(s):  
Dna Binding ◽  
Binding Protein ◽  
Prediction Method ◽  
Dna Binding Protein ◽  
Support Vector ◽  
Classification Technique ◽  
Protein Prediction ◽  
Wide Range ◽  
Vector Machines ◽  
Benchmark Datasets

DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM) as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.

Single-stranded and double-stranded DNA-binding protein prediction using HMM profiles

2021 ◽  
Vol 612 ◽  
pp. 113954
Author(s):  
Ronesh Sharma ◽  
Shiu Kumar ◽  
Tatsuhiko Tsunoda ◽  
Thirumananseri Kumarevel ◽  
Alok Sharma
Keyword(s):  
Dna Binding ◽  
Binding Protein ◽  
Dna Binding Protein ◽  
Double Stranded Dna ◽  
Protein Prediction

The DNA-binding protein YB-1 is a direct MYCN target and influences repair and resistance in neuroblastoma cell lines

2010 ◽  
Vol 222 (03) ◽  
Author(s):  
S Degen ◽  
S Kuhfittig-Kulle ◽  
JH Schulte ◽  
F Westermann ◽  
A Schramm ◽  
...  
Keyword(s):  
Dna Binding ◽  
Cell Lines ◽  
Binding Protein ◽  
Neuroblastoma Cell ◽  
Dna Binding Protein ◽  
Neuroblastoma Cell Lines
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