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10.1038/srep17841 http://scihub22266oqcxt.onion/10.1038/srep17841 C4683538!4683538 !26680653     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26680653 &cmd=llinks): Failed to open stream: HTTP request failed! 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Hidden Markov induced Dynamic Bayesian Network for recovering time evolving gene regulatory networks |pdf=|usr=}}{{26680653 }} gab.com Text Hidden Markov induced Dynamic Bayesian Network for recovering time evolving gene regulatory networks JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=26680653 #FOAvip Dynamic Bayesian Networks (DBN) have been widely used to recover gene regulatory relationships from time-series data in computational systems biology. Its standard assumption is 'stationarity', and therefore, several research efforts have been recently proposed to relax this restriction. However, those methods suffer from three challenges: long running time, low accuracy and reliance on parameter settings. To address these problems, we propose a novel non-stationary DBN model by extending each hidden node of Hidden Markov Model into a DBN (called HMDBN), which properly handles the underlying time-evolving networks. Correspondingly, an improved structural EM algorithm is proposed to learn the HMDBN. It dramatically reduces searching space, thereby substantially improving computational efficiency. Additionally, we derived a novel generalized Bayesian Information Criterion under the non-stationary assumption (called BWBIC), which can help significantly improve the reconstruction accuracy and largely reduce over-fitting. Moreover, the re-estimation formulas for all parameters of our model are derived, enabling us to avoid reliance on parameter settings. Compared to the state-of-the-art methods, the experimental evaluation of our proposed method on both synthetic and real biological data demonstrates more stably high prediction accuracy and significantly improved computation efficiency, even with no prior knowledge and parameter settings. Twit Text FOAVip Hidden Markov induced Dynamic Bayesian Network for recovering time evolving gene regulatory networks ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=26680653 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26680653 #FOAvip English Wikipedia <ref>{{Cite pmid|26680653 }}</ref> Hidden Markov induced Dynamic Bayesian Network for recovering time evolving gene regulatory networks #MMPMID26680653 Zhu S ; Wang Y Sci Rep 2015[Dec]; 5 (ä): 17841 PMID26680653 show ga Dynamic Bayesian Networks (DBN) have been widely used to recover gene regulatory relationships from time-series data in computational systems biology. Its standard assumption is 'stationarity', and therefore, several research efforts have been recently proposed to relax this restriction. However, those methods suffer from three challenges: long running time, low accuracy and reliance on parameter settings. To address these problems, we propose a novel non-stationary DBN model by extending each hidden node of Hidden Markov Model into a DBN (called HMDBN), which properly handles the underlying time-evolving networks. Correspondingly, an improved structural EM algorithm is proposed to learn the HMDBN. It dramatically reduces searching space, thereby substantially improving computational efficiency. Additionally, we derived a novel generalized Bayesian Information Criterion under the non-stationary assumption (called BWBIC), which can help significantly improve the reconstruction accuracy and largely reduce over-fitting. Moreover, the re-estimation formulas for all parameters of our model are derived, enabling us to avoid reliance on parameter settings. Compared to the state-of-the-art methods, the experimental evaluation of our proposed method on both synthetic and real biological data demonstrates more stably high prediction accuracy and significantly improved computation efficiency, even with no prior knowledge and parameter settings. |*Algorithms [MESH] |*Gene Regulatory Networks [MESH] |Animals [MESH] |Bayes Theorem [MESH] |Drosophila/genetics/growth & development [MESH] |Larva/genetics [MESH] |Markov Chains [MESH] |Muscle, Skeletal/growth & development/metabolism [MESH]Hidden Markov induced Dynamic Bayesian Network for recovering time evo(epmc).pdf DeepDyve Pubget Overpricing