title: A novel method for mining highly imbalanced high-throughput screening data in PubChem.
abstract: BACKGROUND
  The comprehensive information of small molecules and their biological activities in PubChem brings great opportunities for academic researchers. However, mining high-throughput screening (HTS) assay data remains a great challenge given the very large data volume and the highly imbalanced nature with only small number of active compounds compared to inactive compounds. Therefore, there is currently a need for better strategies to work with HTS assay data. Moreover, as luciferase-based HTS technology is frequently exploited in the assays deposited in PubChem, constructing a computational model to distinguish and filter out potential interference compounds for these assays is another motivation.
  
  
  RESULTS
  We used the granular support vector machines (SVMs) repetitive under sampling method (GSVM-RU) to construct an SVM from luciferase inhibition bioassay data that the imbalance ratio of active/inactive is high (1/377). The best model recognized the active and inactive compounds at the accuracies of 86.60% and 88.89 with a total accuracy of 87.74%, by cross-validation test and blind test. These results demonstrate the robustness of the model in handling the intrinsic imbalance problem in HTS data and it can be used as a virtual screening tool to identify potential interference compounds in luciferase-based HTS experiments. Additionally, this method has also proved computationally efficient by greatly reducing the computational cost and can be easily adopted in the analysis of HTS data for other biological systems.
  
  
  BACKGROUND
  Data are publicly available in PubChem with AIDs of 773, 1006 and 1379.
  
  
  BACKGROUND
  ywang@ncbi.nlm.nih.gov; bryant@ncbi.nlm.nih.gov
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA.
authors.forename: Qingliang
authors.initials: Q
authors.lastname: Li
authors.forename: Yanli
authors.initials: Y
authors.lastname: Wang
authors.forename: Stephen H
authors.initials: SH
authors.lastname: Bryant
mesh_terms: D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated; D054852:Small Molecule Libraries; D014481:United States
chemical_list: D054852:Small Molecule Libraries