title: Inferring Disease Associated Phosphorylation Sites via Random Walk on Multi-Layer Heterogeneous Network.
abstract: As protein phosphorylation plays an important role in numerous cellular processes, many studies have been undertaken to analyze phosphorylation-related activities for drug design and disease treatment. However, although progresses have been made in illustrating the relationship between phosphorylation and diseases, no existing method focuses on disease-associated phosphorylation sites prediction. In this work, we proposed a multi-layer heterogeneous network model that makes use of the kinase information to infer disease-phosphorylation site relationship and implemented random walk on the heterogeneous network. Experimental results reveal that multi-layer heterogeneous network model with kinase layer is superior in inferring disease-phosphorylation site relationship when comparing with existing random walk model and common used classification methods.
authors.forename: Xiaoyi
authors.initials: X
authors.lastname: Xu
authors.forename: Minghui
authors.initials: M
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D015415:Biomarkers / Q000378:metabolism; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D010766:Phosphorylation / Q000235:genetics; D010770:Phosphotransferases / Q000235:genetics* / Q000378:metabolism; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods; D020543:Proteome / Q000235:genetics* / Q000378:metabolism*; D020539:Sequence Analysis, Protein / Q000379:methods*; D015398:Signal Transduction / Q000235:genetics
chemical_list: D015415:Biomarkers; D020543:Proteome; D010770:Phosphotransferases