title: Predicting Antimicrobial Peptides by Using Increment of Diversity with Quadratic Discriminant Analysis Method.
abstract: Antimicrobial peptides are crucial components of the innate host defense system of most living organisms and promising candidates for antimicrobial agents. Accurate classification of antimicrobial peptides will be helpful to the discovery of new therapeutic targets. In this work, the Increment of Diversity with Quadratic Discriminant analysis (IDQD) was presented to classify antifungal and antibacterial peptides based on primary sequence information. In the jackknife test, the proposed IDQD model yields an accuracy of 86.02 percent with the sensitivity of 74.31 percent and specificity of 92.79 percent for identifying antimicrobial peptides, which is superior to other state-of-the-art methods. This result suggests that the proposed IDQD model can be efficiently used to antimicrobial peptide classification.
authors.forename: Pengmian
authors.initials: P
authors.lastname: Feng
authors.forename: Zhenyi
authors.initials: Z
authors.lastname: Wang
authors.forename: Xiaoyu
authors.initials: X
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry*; D000900:Anti-Bacterial Agents / Q000737:chemistry; D000935:Antifungal Agents / Q000737:chemistry; D023181:Antimicrobial Cationic Peptides / Q000737:chemistry*; D001419:Bacteria / Q000187:drug effects; D019295:Computational Biology / Q000379:methods; D004151:Dipeptides / Q000737:chemistry*; D016002:Discriminant Analysis; D055808:Drug Discovery / Q000379:methods*; D000069550:Machine Learning; D008958:Models, Molecular; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D000596:Amino Acids; D000900:Anti-Bacterial Agents; D000935:Antifungal Agents; D023181:Antimicrobial Cationic Peptides; D004151:Dipeptides