title: Development of a Web-Enabled SVR-Based Machine Learning Platform and its Application on Modeling Transgene Expression Activity of Aminoglycoside-Derived Polycations.
abstract: OBJECTIVE
  Support Vector Regression (SVR) has become increasingly popular in cheminformatics modeling. As a result, SVR-based machine learning algorithms, including Fuzzy-SVR and Least Square-SVR (LS-SVR) have been developed and applied in various research areas. However, at present, few downloadable packages or public-domain software are available for these algorithms. To address this need, we developed the Support vector regression-based Online Learning Equipment (SOLE) web tool (available at http://reccr.chem.rpi.edu/SOLE/index.html) as an online learning system to support predictive cheminformatics and materials informatics studies.
  
  
  RESULTS
  In this work, we employed the SOLE system to model transgene expression efficacy of polymers obtained from aminoglycoside antibiotics, which allowed the results of several modeling approaches to be easily compared. All models had test set r2 of 0.96-0.98 and test set R2 of 0.79-0.84. Y-scrambling test showed the models were stable and not over-fitted.
  
  
  CONCLUSIONS
  SOLE has a user-friendly interface and includes routine elements of performing QSAR/QSPR studies that can be applied in various research areas. It utilizes rational and sophisticated feature selection, model selection and model evaluation processes.
authors.affiliation: Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, United States.
authors.forename: Zhuo
authors.initials: Z
authors.lastname: Zhen
authors.affiliation: Chemical Engineering, Arizona State University, Tempe, AZ 85287-6106, United States.
authors.forename: Thrimoorthy
authors.initials: T
authors.lastname: Potta
authors.affiliation: Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, United States.
authors.forename: Nicholas A
authors.initials: NA
authors.lastname: Lanzillo
authors.affiliation: Chemical Engineering, Arizona State University, Tempe, AZ 85287-6106, United States.
authors.forename: Kaushal
authors.initials: K
authors.lastname: Rege
authors.affiliation: School of Science, Rensselaer Polytechnic Institute, 1C05 Jonsson-Rowland Science Center, 110 Eighth Street, Troy, NY 12180, United States.
authors.forename: Curt M
authors.initials: CM
authors.lastname: Breneman
mesh_terms: D000465:Algorithms; D000617:Aminoglycosides / Q000737:chemistry*; D000900:Anti-Bacterial Agents / Q000737:chemistry; D045744:Cell Line, Tumor; D006801:Humans; D016018:Least-Squares Analysis; D000069550:Machine Learning; D008954:Models, Biological; D011073:Polyamines / Q000737:chemistry*; D000071228:Polyelectrolytes; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis; D012984:Software; D014162:Transfection / Q000379:methods; D019076:Transgenes
chemical_list: D000617:Aminoglycosides; D000900:Anti-Bacterial Agents; D011073:Polyamines; D000071228:Polyelectrolytes
keywords: QSAR; QSPR; Software; machine learning; regression; support vector machine