Deep-4mCW2V: A sequence-based predictor to identify N4-methylcytosine sites in Escherichia coli

Zulfiqar, Hasan; Sun, Zi-Jie; Huang, Qin-Lai; Yuan, Shi-Shi; Lv, Hao; Lin, Hao; Li, Yanwen

doi:10.1016/j.ymeth.2021.07.011

Cited by 53 publications

(29 citation statements)

References 71 publications

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“…For classification, we examined a number of classifiers, including Support Vector Machine (SVM; Tang et al, 2017 ), K Nearest Neighbor (KNN; Zuo et al, 2013 ; Zulfiqar et al, 2021a ), Random Forest (RF), and Multi-layer Perceptron (MLP) for training the model. The following sections will introduce these classifiers briefly.…”

Section: Methodsmentioning

confidence: 99%

iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy

et al. 2022

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Thermophilic proteins have important application value in biotechnology and industrial processes. The correct identification of thermophilic proteins provides important information for the application of these proteins in engineering. The identification method of thermophilic proteins based on biochemistry is laborious, time-consuming, and high cost. Therefore, there is an urgent need for a fast and accurate method to identify thermophilic proteins. Considering this urgency, we constructed a reliable benchmark dataset containing 1,368 thermophilic and 1,443 non-thermophilic proteins. A multi-layer perceptron (MLP) model based on a multi-feature fusion strategy was proposed to discriminate thermophilic proteins from non-thermophilic proteins. On independent data set, the proposed model could achieve an accuracy of 96.26%, which demonstrates that the model has a good application prospect. In order to use the model conveniently, a user-friendly software package called iThermo was established and can be freely accessed at http://lin-group.cn/server/iThermo/index.html. The high accuracy of the model and the practicability of the developed software package indicate that this study can accelerate the discovery and engineering application of thermally stable proteins.

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Section: Methodsmentioning

confidence: 99%

iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy

et al. 2022

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“…They not only include the distinctive approach for feature extraction of proteomic data but also extend to feature vectors which include, “Functional Domain” mode, “Gene Ontology” mode, and “Sequential Evolution” or “PSSM” mode. Inspired by the complementary outcome of utilizing “PseAAC” to handle the sequences of peptide or protein, the proposed strategy of “PseAAC” was continued to Pseudo K-tuple Nucleotide Composition (PseKNC) for developing and achieving different feature vectors for RNA/DNA that have confirmed very favourable as well [ 64 – 70 ]. Especially, recently, an advanced web server named “Pse-in-One” [ 71 ] and “Pse-in-One 2.0” [ 72 ], which is its advanced version and can be utilized in generating any required protein/peptide vector and sequences of DNA and RNA according to the requirement of the users.…”

Section: Materials and Methodologymentioning

confidence: 99%

DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features

Barukab

Khan

et al. 2022

Applied Bionics and Biomechanics

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In the domain of genome annotation, the identification of DNA-binding protein is one of the crucial challenges. DNA is considered a blueprint for the cell. It contained all necessary information for building and maintaining the trait of an organism. It is DNA, which makes a living thing, a living thing. Protein interaction with DNA performs an essential role in regulating DNA functions such as DNA repair, transcription, and regulation. Identification of these proteins is a crucial task for understanding the regulation of genes. Several methods have been developed to identify the binding sites of DNA and protein depending upon the structures and sequences, but they were costly and time-consuming. Therefore, we propose a methodology named “DNAPred_Prot”, which uses various position and frequency-dependent features from protein sequences for efficient and effective prediction of DNA-binding proteins. Using testing techniques like 10-fold cross-validation and jackknife testing an accuracy of 94.95% and 95.11% was yielded, respectively. The results of SVM and ANN were also compared with those of a random forest classifier. The robustness of the proposed model was evaluated by using the independent dataset PDB186, and an accuracy of 91.47% was achieved by it. From these results, it can be predicted that the suggested methodology performs better than other extant methods for the identification of DNA-binding proteins.

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“…Generally, feature encoding plays a crucial role for machine learning in model construction [ 22 – 28 ]. The feature encoding method determines the degree of sequence information mining.…”

Section: Methodsmentioning

confidence: 99%

Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features

Liu

Chen

Gao

et al. 2022

Computational and Mathematical Methods in Medicine

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Helicobacter pylori (H. pylori) is the most common risk factor for gastric cancer worldwide. The membrane proteins of the H. pylori are involved in bacterial adherence and play a vital role in the field of drug discovery. Thus, an accurate and cost-effective computational model is needed to predict the uncharacterized membrane proteins of H. pylori. In this study, a reliable benchmark dataset consisted of 114 membrane and 219 nonmembrane proteins was constructed based on UniProt. A support vector machine- (SVM-) based model was developed for discriminating H. pylori membrane proteins from nonmembrane proteins by using sequence information. Cross-validation showed that our method achieved good performance with an accuracy of 91.29%. It is anticipated that the proposed model will be useful for the annotation of H. pylori membrane proteins and the development of new anti-H. pylori agents.

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Deep-4mCW2V: A sequence-based predictor to identify N4-methylcytosine sites in Escherichia coli

Cited by 53 publications

References 71 publications

iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy

iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy

DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features

Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features

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