Comput Math Methods Med - Modified logistic regression models using gene coexpression and clinical features to predict prostate cancer progression.

Tópicos

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Resumo

Predicting disease progression is one of the most challenging problems in prostate cancer research. Adding gene expression data to prediction models that are based on clinical features has been proposed to improve accuracy. In the current study, we applied a logistic regression (LR) model combining clinical features and gene co-expression data to improve the accuracy of the prediction of prostate cancer progression. The top-scoring pair (TSP) method was used to select genes for the model. The proposed models not only preserved the basic properties of the TSP algorithm but also incorporated the clinical features into the prognostic models. Based on the statistical inference with the iterative cross validation, we demonstrated that prediction LR models that included genes selected by the TSP method provided better predictions of prostate cancer progression than those using clinical variables only and/or those that included genes selected by the one-gene-at-a-time approach. Thus, we conclude that TSP selection is a useful tool for feature (and/or gene) selection to use in prognostic models and our model also provides an alternative for predicting prostate cancer progression.

Resumo Limpo

predict diseas progress one challeng problem prostat cancer research ad gene express data predict model base clinic featur propos improv accuraci current studi appli logist regress lr model combin clinic featur gene coexpress data improv accuraci predict prostat cancer progress topscor pair tsp method use select gene model propos model preserv basic properti tsp algorithm also incorpor clinic featur prognost model base statist infer iter cross valid demonstr predict lr model includ gene select tsp method provid better predict prostat cancer progress use clinic variabl andor includ gene select onegeneatatim approach thus conclud tsp select use tool featur andor gene select use prognost model model also provid altern predict prostat cancer progress

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