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Over the years, the way customers measure value has changed drastically. If companies still focusing on pure product quality or cost, will gradually lose competitiveness. Considered as a solution to the strategy, the product service system combines tangible products, intangible services, and back-end support systems to reduce the risk of uncertainties and meet diverse customer needs. Although there are many studies on product service systems, there are still opportunities of improving design methods that can provide different content in response to changing need. In view of customization, this paper utilizing text analytic technique to capture PSS improving opportunity, and realizing customization with a MAS-based recommend system.

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Targeted therapies and chemotherapies are prevalent in cancer treatment. Identification of predictive markers to stratify cancer patients who will respond to these therapies remains challenging because patient drug response data are limited. As large amounts of drug response data have been generated by cell lines, methods to efficiently translate cell-line-trained predictors to human tumors will be useful in clinical practice. Here, we propose versatile feature selection procedures that can be combined with any classifier. For demonstration, we combined the feature selection procedures with a (linear) logit model and a (non-linear) K-nearest neighbor and trained these on cell lines to result in LogitDA and KNNDA, respectively. We show that LogitDA/KNNDA significantly outperforms existing methods, e.g., a logistic model and a deep learning method trained by thousands of genes, in prediction AUC (0.70–1.00 for seven of the ten drugs tested) and is interpretable. This may be due to the fact that sample sizes are often limited in the area of drug response prediction. We further derive a novel adjustment on the prediction cutoff for LogitDA to yield a prediction accuracy of 0.70–0.93 for seven drugs, including erlotinib and cetuximab, whose pathways relevant to anti-cancer therapies are also uncovered. These results indicate that our methods can efficiently translate cell-line-trained predictors into tumors.