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StackPDB: predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier
Q Zhang, P Liu, X Wang, Y Zhang, Y Han, B Yu - Applied Soft Computing, 2021 - Elsevier
DNA-binding proteins (DBPs) not only play an important role in all aspects of genetic
activities such as DNA replication, recombination, repair, and modification but also are used …
activities such as DNA replication, recombination, repair, and modification but also are used …
Identification of DNA-binding proteins by combining auto-cross covariance transformation and ensemble learning
B Liu, S Wang, Q Dong, S Li… - IEEE transactions on …, 2016 - ieeexplore.ieee.org
DNA-binding proteins play a pivotal role in various intra-and extra-cellular activities ranging
from DNA replication to gene expression control. With the rapid development of next …
from DNA replication to gene expression control. With the rapid development of next …
Using Chou's general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains
SM Krishnan - Journal of theoretical biology, 2018 - Elsevier
The receptor-associated protein (RAP) is an inhibitor of endocytic receptors that belong to
the lipoprotein receptor gene family. In this study, a computational approach was tried to find …
the lipoprotein receptor gene family. In this study, a computational approach was tried to find …
Identification of DNA-binding proteins by auto-cross covariance transformation
Q Dong, S Wang, K Wang, X Liu… - 2015 IEEE International …, 2015 - ieeexplore.ieee.org
DNA-binding proteins play a pivotal role in various intra-and extra-cellular activities ranging
from DNA replication to gene expression control. With the rapid development of next …
from DNA replication to gene expression control. With the rapid development of next …
Enabling full‐length evolutionary profiles based deep convolutional neural network for predicting DNA‐binding proteins from sequence
Sequence based DNA‐binding protein (DBP) prediction is a widely studied biological
problem. Sliding windows on position specific substitution matrices (PSSMs) rows predict …
problem. Sliding windows on position specific substitution matrices (PSSMs) rows predict …
UMAP-DBP: an improved DNA-binding proteins prediction method based on uniform manifold approximation and projection
J Wang, S Zhang, H Qiao, J Wang - The Protein Journal, 2021 - Springer
DNA-binding proteins play a vital role in cellular processes. It is an extremely urgent to
develop a high-throughput method for efficiently identifying DNA-binding proteins. According …
develop a high-throughput method for efficiently identifying DNA-binding proteins. According …
DNA-binding protein prediction using plant specific support vector machines: validation and application of a new genome annotation tool
There are currently 151 plants with draft genomes available but levels of functional
annotation for putative protein products are low. Therefore, accurate computational …
annotation for putative protein products are low. Therefore, accurate computational …
[HTML][HTML] Small-angle scattering and multifractal analysis of DNA sequences
EM Anitas - International Journal of Molecular Sciences, 2020 - mdpi.com
The arrangement of A, C, G and T nucleotides in large DNA sequences of many prokaryotic
and eukaryotic cells exhibit long-range correlations with fractal properties. Chaos game …
and eukaryotic cells exhibit long-range correlations with fractal properties. Chaos game …
PSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional …
L Li, X Cui, S Yu, Y Zhang, Z Luo, H Yang, Y Zhou… - PLoS …, 2014 - journals.plos.org
Protein structure prediction is critical to functional annotation of the massively accumulated
biological sequences, which prompts an imperative need for the development of high …
biological sequences, which prompts an imperative need for the development of high …
A kernelized non-parametric classifier based on feature ranking in anisotropic Gaussian kernel
Non-parametric methods make no assumptions about the form of data distribution and
estimate it directly from the data. Kernel density estimation is a non-parametric method …
estimate it directly from the data. Kernel density estimation is a non-parametric method …