%0 Journal Article %1 perakakis2018omics %A Perakakis, Nikolaos %A Yazdani, Alireza %A Karniadakis, George E %A Mantzoros, Christos %D 2018 %I Elsevier %J Metabolism-Clinical and Experimental %K multiomics omics descritpion methods machine learning review survey editorial citedby:scholar:count:25 citedby:scholar:timestamp:2020-8-6 %P A1--A9 %T Omics, big data and machine learning as tools to propel understanding of biological mechanisms and to discover novel diagnostics and therapeutics %V 87

@article{perakakis2018omics, added-at = {2020-08-06T03:53:02.000+0200}, author = {Perakakis, Nikolaos and Yazdani, Alireza and Karniadakis, George E and Mantzoros, Christos}, biburl = {https://www.bibsonomy.org/bibtex/29329f845a064e10d5413ce215a33e1f6/becker}, interhash = {26b33624c6d739a5507469d6c943f9b0}, intrahash = {9329f845a064e10d5413ce215a33e1f6}, journal = {Metabolism-Clinical and Experimental}, keywords = {multiomics omics descritpion methods machine learning review survey editorial citedby:scholar:count:25 citedby:scholar:timestamp:2020-8-6}, pages = {A1--A9}, publisher = {Elsevier}, timestamp = {2020-08-06T03:53:02.000+0200}, title = {Omics, big data and machine learning as tools to propel understanding of biological mechanisms and to discover novel diagnostics and therapeutics}, volume = 87, year = 2018 }