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10.1155/2015/846942 http://scihub22266oqcxt.onion/10.1155/2015/846942 C4633688!4633688 !26579207     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26579207 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Comput+Math+Methods+Med 2015 ; 2015 (ä): 846942 Nephropedia Template TP {{tp|p=26579207 |t=2015. NMFBFS: A NMF-Based Feature Selection Method in Identifying Pivotal Clinical Symptoms of Hepatocellular Carcinoma |pdf=|usr=}}{{26579207 }} gab.com Text NMFBFS: A NMF-Based Feature Selection Method in Identifying Pivotal Clinical Symptoms of Hepatocellular Carcinoma JO: Comput Math Methods Med http://www.kidney.de/pget.php?&tw=1&pmid=26579207 #FOAvip BACKGROUND: Hepatocellular carcinoma (HCC) is a highly aggressive malignancy. Traditional Chinese Medicine (TCM), with the characteristics of syndrome differentiation, plays an important role in the comprehensive treatment of HCC. This study aims to develop a nonnegative matrix factorization- (NMF-) based feature selection approach (NMFBFS) to identify potential clinical symptoms for HCC patient stratification. METHODS: The NMFBFS approach consisted of three major steps. Firstly, statistics-based preliminary feature screening was designed to detect and remove irrelevant symptoms. Secondly, NMF was employed to infer redundant symptoms. Based on NMF-derived basis matrix, we defined a novel similarity measurement of intersymptoms. Finally, we converted each group of redundant symptoms to a new single feature so that the dimension was further reduced. RESULTS: Based on a clinical dataset consisting of 407 patient samples of HCC with 57 symptoms, NMFBFS approach detected 8 irrelevant symptoms and then identified 16 redundant symptoms within 6 groups. Finally, an optimal feature subset with 39 clinical features was generated after compressing the redundant symptoms by groups. The validation of classification performance shows that these 39 features obviously improve the prediction accuracy of HCC patients. CONCLUSIONS: Compared with other methods, NMFBFS has obvious advantages in identifying important clinical features of HCC. Twit Text FOAVip NMFBFS: A NMF-Based Feature Selection Method in Identifying Pivotal Clinical Symptoms of Hepatocellular Carcinoma ????Comput Math Methods Med http://www.kidney.de/pget.php?&tw=1&pmid=26579207 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26579207 #FOAvip English Wikipedia <ref>{{Cite pmid|26579207 }}</ref> NMFBFS: A NMF-Based Feature Selection Method in Identifying Pivotal Clinical Symptoms of Hepatocellular Carcinoma #MMPMID26579207 Ji Z ; Meng G ; Huang D ; Yue X ; Wang B Comput Math Methods Med 2015[]; 2015 (ä): 846942 PMID26579207 show ga BACKGROUND: Hepatocellular carcinoma (HCC) is a highly aggressive malignancy. Traditional Chinese Medicine (TCM), with the characteristics of syndrome differentiation, plays an important role in the comprehensive treatment of HCC. This study aims to develop a nonnegative matrix factorization- (NMF-) based feature selection approach (NMFBFS) to identify potential clinical symptoms for HCC patient stratification. METHODS: The NMFBFS approach consisted of three major steps. Firstly, statistics-based preliminary feature screening was designed to detect and remove irrelevant symptoms. Secondly, NMF was employed to infer redundant symptoms. Based on NMF-derived basis matrix, we defined a novel similarity measurement of intersymptoms. Finally, we converted each group of redundant symptoms to a new single feature so that the dimension was further reduced. RESULTS: Based on a clinical dataset consisting of 407 patient samples of HCC with 57 symptoms, NMFBFS approach detected 8 irrelevant symptoms and then identified 16 redundant symptoms within 6 groups. Finally, an optimal feature subset with 39 clinical features was generated after compressing the redundant symptoms by groups. The validation of classification performance shows that these 39 features obviously improve the prediction accuracy of HCC patients. CONCLUSIONS: Compared with other methods, NMFBFS has obvious advantages in identifying important clinical features of HCC. |Carcinoma, Hepatocellular/*diagnosis/therapy [MESH] |Computational Biology [MESH] |Computer Simulation [MESH] |Databases, Factual/statistics & numerical data [MESH] |Diagnosis, Computer-Assisted/methods/statistics & numerical data [MESH] |Female [MESH] |Humans [MESH] |Liver Neoplasms/*diagnosis/therapy [MESH] |Male [MESH]NMFBFS: A NMF-Based Feature Selection Method in Identifying Pivotal Cl(epmc).pdf DeepDyve Pubget Overpricing