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10.1186/1752-0509-7-S6-S10 http://scihub22266oqcxt.onion/10.1186/1752-0509-7-S6-S10 C4029265!4029265 !24565108     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 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\24565108 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       BMC+Syst+Biol 2013 ; 7 Suppl 6 (Suppl 6 ): S10 Nephropedia Template TP {{tp|p=24565108 |t=2013. CGPredictor: a systematic integrated analytic tool for mining and examining genome-scale cancer independent prognostic epigenetic marker panels |pdf=|usr=}}{{24565108 }} gab.com Text CGPredictor: a systematic integrated analytic tool for mining and examining genome-scale cancer independent prognostic epigenetic marker panels JO: BMC Syst Biol http://www.kidney.de/pget.php?&tw=1&pmid=24565108 #FOAvip BACKGROUND: Tumor biomarkers are potentially useful in several ways such as the identification of individuals at increased risk of developing cancer, in screening for early malignancies and in aiding cancer diagnoses; tumor biomarkers may also be used for determining prognosis, predicting therapeutic response, patient tracking following curative surgery for cancer and for monitoring therapy. Epigenetic alterations, especially aberrant DNA methylation, are recognized as common molecular alterations in a variety of tumors and also occur during the development of tumors. The Cancer Grade Predictor (CGPredictor) is an extendable package with functions designed to facilitate systematic integrated and rapid analysis of high-throughput methylation through the use of most self-similarity subgroups of patients supported by various validating examinations with regarded to survival outcome to obtain the identity of the target predictor. RESULTS: We used high-grade serous ovarian cancer (HGSOC) and invasive breast carcinoma (BRCA) to demonstrate the usefulness of the CGPredictor package. The clustering results and the identity predictors worked well and efficiently in producing significant results after various tests were used to validate the usefulness of CGPredictor package. Also, some of the markers for either the HGSOC or BRCA marker panel have been previously reported to reveal significant results. Even when performed using a different platform with an independent large population BRCA dataset for validation, the identity predictor provided an accurate assessment of patient conditions and produced significant results. CONCLUSIONS: CGPredictor package is not a customized analysis tool designed specifically for the identification of only one or a few specific types of cancer but can be applied more broadly; moreover, the results indicate that the extracted predictors may worthy of consideration for further clinical testing to identify their potential usefulness for clinical molecular diagnosis and targeted treatments of patients with HGSOC and BRCA. So, the use of CGPredictor is feasible for examining the statistical significance of specific markers of interest and shows great potential for use with other types of cancers for cancer biomarker mining. Twit Text FOAVip CGPredictor: a systematic integrated analytic tool for mining and examining genome-scale cancer independent prognostic epigenetic marker panels ????BMC Syst Biol http://www.kidney.de/pget.php?&tw=1&pmid=24565108 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24565108 #FOAvip English Wikipedia <ref>{{Cite pmid|24565108 }}</ref> CGPredictor: a systematic integrated analytic tool for mining and examining genome-scale cancer independent prognostic epigenetic marker panels #MMPMID24565108 Cheng WS ; Chiang JH BMC Syst Biol 2013[]; 7 Suppl 6 (Suppl 6 ): S10 PMID24565108 show ga BACKGROUND: Tumor biomarkers are potentially useful in several ways such as the identification of individuals at increased risk of developing cancer, in screening for early malignancies and in aiding cancer diagnoses; tumor biomarkers may also be used for determining prognosis, predicting therapeutic response, patient tracking following curative surgery for cancer and for monitoring therapy. Epigenetic alterations, especially aberrant DNA methylation, are recognized as common molecular alterations in a variety of tumors and also occur during the development of tumors. The Cancer Grade Predictor (CGPredictor) is an extendable package with functions designed to facilitate systematic integrated and rapid analysis of high-throughput methylation through the use of most self-similarity subgroups of patients supported by various validating examinations with regarded to survival outcome to obtain the identity of the target predictor. RESULTS: We used high-grade serous ovarian cancer (HGSOC) and invasive breast carcinoma (BRCA) to demonstrate the usefulness of the CGPredictor package. The clustering results and the identity predictors worked well and efficiently in producing significant results after various tests were used to validate the usefulness of CGPredictor package. Also, some of the markers for either the HGSOC or BRCA marker panel have been previously reported to reveal significant results. Even when performed using a different platform with an independent large population BRCA dataset for validation, the identity predictor provided an accurate assessment of patient conditions and produced significant results. CONCLUSIONS: CGPredictor package is not a customized analysis tool designed specifically for the identification of only one or a few specific types of cancer but can be applied more broadly; moreover, the results indicate that the extracted predictors may worthy of consideration for further clinical testing to identify their potential usefulness for clinical molecular diagnosis and targeted treatments of patients with HGSOC and BRCA. So, the use of CGPredictor is feasible for examining the statistical significance of specific markers of interest and shows great potential for use with other types of cancers for cancer biomarker mining. |*Epigenesis, Genetic [MESH] |Aged [MESH] |Biomarkers, Tumor/genetics [MESH] |Breast Neoplasms/*diagnosis/*genetics/pathology [MESH] |Cluster Analysis [MESH] |DNA Methylation [MESH] |Data Mining/*methods [MESH] |Female [MESH] |Genomics/*methods [MESH] |Humans [MESH] |Middle Aged [MESH] |Neoplasm Grading [MESH] |Ovarian Neoplasms/*diagnosis/*genetics/pathology [MESH] |Prognosis [MESH]CGPredictor: a systematic integrated analytic tool for mining and exam(epmc).pdf DeepDyve Pubget Overpricing