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10.1016/B978-0-12-801238-3.11708-8 http://scihub22266oqcxt.onion/10.1016/B978-0-12-801238-3.11708-8 C7278532!7278532!C7278532     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       Systems+Medicine 2021 ; ä (ä): 222-3 Nephropedia Template TP {{tp|p=C7278532|t=2021. Overview: Data Generation Techniques: From Omics to Personalized Approaches and Clinical Care |pdf=|usr=}}{{C7278532}} gab.com Text Overview: Data Generation Techniques: From Omics to Personalized Approaches and Clinical Care JO: Systems Medicine http://www.kidney.de/pget.php?&tw=1&pmid=C7278532 #FOAvip In efforts to better understand human complex pathologies we are faced with raising numbers of data, from different resources, different experimental models, and different patients. It is acknowledged that one of the gaps is making data available for future research, taking into account the FAIR (findable, reusable, interoperable, and reproducible) principles. On the other hand, it should not be forgotten that data generation techniques are of equal importance. In this section, we discuss a variety of data-based approaches on different multifactorial diseases as use cases. Transcriptomics and other omic technologies hold a great potential not only for improved diagnostics of complex diseases, but also for improved prognosis and treatment optimizations where network enrichment methods can be applied to decipher mechanisms and find disease overlaps on the molecular level. New diagnostic and prognostic biomarkers remain a need where multiomics proved its essentiality. An important part of this section is also the clinical view. Clinicians and other health scientists are faced by challenges in daily practice to better understand and manage patients with the aid of available data. Big data in clinical practice is a big issue, especially in primary care where systems approaches are applied and realized as personalized medicine. One take-home message from this section is focused on patients as a resource of the data: we should not forget the ethical principles and the humanity. A human individual is much more than a collection of data. Twit Text FOAVip Overview: Data Generation Techniques: From Omics to Personalized Approaches and Clinical Care ????Systems Medicine http://www.kidney.de/pget.php?&tw=1&pmid=C7278532 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=C7278532 #FOAvip English Wikipedia <ref>{{Cite pmid|C7278532}}</ref> Overview: Data Generation Techniques: From Omics to Personalized Approaches and Clinical Care #MMPMIDC7278532 Rozman D Systems Medicine 2021[]; ä (ä): 222-3 PMIDC7278532show ga In efforts to better understand human complex pathologies we are faced with raising numbers of data, from different resources, different experimental models, and different patients. It is acknowledged that one of the gaps is making data available for future research, taking into account the FAIR (findable, reusable, interoperable, and reproducible) principles. On the other hand, it should not be forgotten that data generation techniques are of equal importance. In this section, we discuss a variety of data-based approaches on different multifactorial diseases as use cases. Transcriptomics and other omic technologies hold a great potential not only for improved diagnostics of complex diseases, but also for improved prognosis and treatment optimizations where network enrichment methods can be applied to decipher mechanisms and find disease overlaps on the molecular level. New diagnostic and prognostic biomarkers remain a need where multiomics proved its essentiality. An important part of this section is also the clinical view. Clinicians and other health scientists are faced by challenges in daily practice to better understand and manage patients with the aid of available data. Big data in clinical practice is a big issue, especially in primary care where systems approaches are applied and realized as personalized medicine. One take-home message from this section is focused on patients as a resource of the data: we should not forget the ethical principles and the humanity. A human individual is much more than a collection of data. äOverview: Data Generation Techniques: From Omics to Personalized Appro(epmc).pdf DeepDyve Pubget Overpricing