ArticleScientific reports2018
A Novel Modeling in Mathematical Biology for Classification of Signal Peptides.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- A Framework for Prediction of Oncogenomic Progression Aiding Personalized Treatment of Gastric Cancer.Diagnostics (Basel, Switzerland) · 2023Article
- Proteome-Scale Screening to Identify High-Expression Signal Peptides with Minimal N-Terminus Biases via Yeast Display.ACS synthetic biology · 2022Article
- Integrating LASSO Feature Selection and Soft Voting Classifier to Identify Origins of Replication Sites.Current genomics · 2022Article
- Article
- iSUMOK-PseAAC: prediction of lysine sumoylation sites using statistical moments and Chou's PseAAC.PeerJ · 2021Article
- iHyd-LysSite (EPSV): Identifying Hydroxylysine Sites in Protein Using Statistical Formulation by Extracting Enhanced Position and Sequence Variant Feature Technique.Current genomics · 2020Article
- miRNALoc: predicting miRNA subcellular localizations based on principal component scores of physico-chemical properties and pseudo compositions of di-nucleotides.Scientific reports · 2020Article
- A Linear Regression Predictor for Identifying NMolecular therapy. Nucleic acids · 2019Article
- iMethylK_pseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAACCurrent genomics · 2019Article
- iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical MomentsCurrent genomics · 2019Article
- iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites.Briefings in bioinformatics · 2019Review
- iHyd-PseAAC (EPSV): Identifying Hydroxylation Sites in Proteins by Extracting Enhanced Position and Sequence Variant FeatureCurrent genomics · 2019Article
- iNuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou's PseAAC to pseudo-tri-nucleotide composition.Molecular genetics and genomics : MGG · 2019Article
- Article
- Plant protection product dose rate estimation in apple orchards using a fuzzy logic system.PloS one · 2019Article
- PhoglyStruct: Prediction of phosphoglycerylated lysine residues using structural properties of amino acids.Scientific reports · 2018Article
- Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome.Bioinformatics (Oxford, England) · 2018Article
- Predicting membrane proteins and their types by extracting various sequence features into Chou's general PseAAC.Molecular biology reports · 2018Article
- iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC.Molecular biology reports · 2018Article
- iRNA-3typeA: Identifying Three Types of Modification at RNA's Adenosine Sites.Molecular therapy. Nucleic acids · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The molecular structure of macromolecules in living cells is ambiguous unless we classify them in a scientific manner. Signal peptides are of vital importance in determining the behavior of newly formed proteins towards their destined path in cellular and extracellular location in both eukaryotes and prokaryotes. In the present research work, a novel method is offered to foreknow the behavior of signal peptides and determine their cleavage site. The proposed model employs neural networks using isolated sets of prokaryote and eukaryote primary sequences. Protein sequences are classified as secretory or non-secretory in order to investigate secretory proteins and their signal peptides. In comparison with the previous prediction tools, the proposed algorithm is more rigorous, well-organized, significantly appropriate and highly accurate for the examination of signal peptides even in extensive collection of protein sequences.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.