title: Identification of novel type III effectors using latent Dirichlet allocation.
abstract: Among the six secretion systems identified in Gram-negative bacteria, the type III secretion system (T3SS) plays important roles in the disease development of pathogens. T3SS has attracted a great deal of research interests. However, the secretion mechanism has not been fully understood yet. Especially, the identification of effectors (secreted proteins) is an important and challenging task. This paper adopts machine learning methods to identify type III secreted effectors (T3SEs). We extract features from amino acid sequences and conduct feature reduction based on latent semantic information by using latent Dirichlet allocation model. The experimental results on Pseudomonas syringae data set demonstrate the good performance of the new methods.
authors.affiliation: Department of Computer Science and Engineering, Information Engineering College, Shanghai Maritime University, 1550 Haigang Avenue, Shanghai 201306, China. yangy09@gmail.com
authors.forename: Yang
authors.initials: Y
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry; D001185:Artificial Intelligence; D001426:Bacterial Proteins / Q000737:chemistry*; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D016680:Genome, Bacterial; D006090:Gram-Negative Bacteria / Q000737:chemistry / Q000378:metabolism*; D008954:Models, Biological; D008962:Models, Theoretical; D009323:Natural Language Processing; D011506:Proteins / Q000737:chemistry; D044224:Pseudomonas syringae / Q000378:metabolism; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D000596:Amino Acids; D001426:Bacterial Proteins; D011506:Proteins