Response modeling with support vector regression
Expert Systems with Applications: An International Journal
Support Vector Machine for soft fault location in electrical circuits
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
New approach to predicting proconvulsant activity with the use of Support Vector Regression
Computers in Biology and Medicine
Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor
Computer Methods and Programs in Biomedicine
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Drug interactions are an important issue of efficacious and safe pharmacotherapy. Although the use of drug combinations carries the potential risk of enhanced toxicity, when carefully introduced it enables to optimize the therapy and achieve pharmacological effects at doses lower than those of single agents. In view of the development of novel analgesic compounds for the neuropathic pain treatment little is known about their influence on the efficacy of currently used analgesic drugs. Below we describe the preliminary evaluation of support vector machine in the regression mode (SVR) application for the prediction of maximal antiallodynic effect of a new derivative of dihydrofuran-2-one (LPP1) used in combination with pregabalin (PGB) in the streptozocin-induced neuropathic pain model in mice. Based on SVR the most effective doses of co-administered LPP1 (4mg/kg) and PGB (1mg/kg) were predicted to cause the paw withdrawal threshold at 6.7g in the von Frey test. In vivo for the same combination of doses the paw withdrawal was observed at 6.5g, which confirms good predictive properties of SVR.