title: Hybrid imbalanced data classifier models for computational discovery of antibiotic drug targets.
abstract: Identification of drug candidates is an important but also difficult process. Given drug resistance bacteria that we face, this process has become more important to identify protein candidates that demonstrate antibacterial activity. The aim of this study is therefore to develop a bioinformatics approach that is more capable of identifying a small but effective set of proteins that are expected to show antibacterial activity, subsequently to be used as antibiotic drug targets. As this is regarded as an imbalanced data classification problem due to smaller number of antibiotic drugs available, a hybrid classification model was developed and applied to the identification of antibiotic drugs. The model was developed by taking into account of various statistical models leading to the development of six different hybrid models. The best model has reached the accuracy of as high as 50% compared to earlier study with the accuracy of less than 1% as far as the proportion of the candidates identified and actual antibiotics in the candidate list is concerned.
authors.forename: Yucel
authors.initials: Y
authors.lastname: Kocyigit
authors.forename: Huseyin
authors.initials: H
authors.lastname: Seker
mesh_terms: D000900:Anti-Bacterial Agents; D019295:Computational Biology / Q000379:methods*; D055808:Drug Discovery / Q000379:methods*; D015233:Models, Statistical; D011506:Proteins / Q000032:analysis / Q000737:chemistry / Q000145:classification
chemical_list: D000900:Anti-Bacterial Agents; D011506:Proteins