2007. Network-based prediction of protein function. Mol. Syst. Biol. 3, 88.Sharan, R., Ulitsky, I., and Shamir, R. (2007). Network-based prediction of protein function. Molecular Systems Biology, 3, 1-13. Shiple
helping identify biologically significant, yet unmapped protein-protein interactions (PPIs). While link prediction methods connect proteins on the basis of biological or
Protein functionHeterogeneous networkNetwork propagationProtein function prediction is a fundamental cornerstone in bioinformatics, providing critical insights into biological processes and disease mechanisms. Despite significant advances, challenges persist due to data sparsity and functional ambiguity. We introduce...
To exploit the predictive power of this network-based prediction prospectively, we first focus on hydrochlorothiazide, an FDA-approved inhibitor on the sodium-chloride symporter for treatment of hypertension28. Our network-based algorithm offers 30 potentially efficacious combinations involving hydrochlorothiazi...
Sequence-based prediction of protein-protein interaction sites by simplified long short-term memory networkProtein-protein interaction sitesImbalance classificationDeep learningSimplified long-short term memory networkProteins often interact with each other and form protein complexes to carry out various ...
Prediction of protein functions based on function-function correlation relations Comput. Biol. Med., 40 (2010), pp. 300-305 View PDFView articleView in ScopusGoogle Scholar [32] L. Chen, C. Chu, X. Kong, T. Huang, Y. Cai Discovery of new candidate genes related to brain development us...
Mosharaf MP, Hassan MM, Ahmed FF, Khatun MS, Moni MA, Mollah MNH (2020) Computational prediction of protein ubiquitination sites mapping on Arabidopsis thaliana. Comput Biol Chem 85:107238. https://doi.org/10.1016/j.compbiolchem.2020.107238 Article CAS PubMed Google Scholar Mukerji SS, Solom...
Proteins play a pivotal role in the diverse array of biological processes, making the precise prediction of protein–protein interaction (PPI) sites critical to numerous disciplines including biology, medicine and pharmacy. While deep learning methods have progressively been implemented for the prediction...
By using position-weight matrices, we predicted binding of the 27 TFs to the gene loci of the top 10% hub genes (Table S3G). Twenty-six out of 27 of these TFs showed significantly enriched binding prediction (p value < 0.05). As a complementary approach, we also applied two gene ...
methods.TwoapproachesforusingDenseCPDpredictionsincomputationalproteindesignwereanalyzed.Theapproachusingthecutoffofaccumulativeprobabilityhadasmallersequencesearchspacecomparedwiththeapproachthatsimplyusesthetop-kpredictionsandthereforeenabledhighersequenceidentityinredesigningthreeproteinswithRosetta.Thenetworkandthedatasetsare...