Comput. Biol. Med. - A knowledge-driven probabilistic framework for the prediction of protein-protein interaction networks.

Tópicos

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Resumo

This study applied a knowledge-driven data integration framework for the inference of protein-protein interactions (PPI). Evidence from diverse genomic features is integrated using a knowledge-driven Bayesian network (KD-BN). Receiver operating characteristic (ROC) curves may not be the optimal assessment method to evaluate a classifier's performance in PPI prediction as the majority of the area under the curve (AUC) may not represent biologically meaningful results. It may be of benefit to interpret the AUC of a partial ROC curve whereby biologically interesting results are represented. Therefore, the novel application of the assessment method referred to as the partial ROC has been employed in this study to assess predictive performance of PPI predictions along with calculating the True positive/false positive rate and true positive/positive rate. By incorporating domain knowledge into the construction of the KD-BN, we demonstrate improvement in predictive performance compared with previous studies based upon the Naive Bayesian approach.

Resumo Limpo

studi appli knowledgedriven data integr framework infer proteinprotein interact ppi evid divers genom featur integr use knowledgedriven bayesian network kdbn receiv oper characterist roc curv may optim assess method evalu classifi perform ppi predict major area curv auc may repres biolog meaning result may benefit interpret auc partial roc curv wherebi biolog interest result repres therefor novel applic assess method refer partial roc employ studi assess predict perform ppi predict along calcul true positivefals posit rate true positiveposit rate incorpor domain knowledg construct kdbn demonstr improv predict perform compar previous studi base upon naiv bayesian approach

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