China 
Africa 
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High Information Content Physiological Biosensors
Published in George K. Knopf, Amarjeet S. Bassi, Smart Biosensor Technology, 2018
Guenter W. Gross, Joseph J. Pancrazio, Kamakshi Gopal
The importance of imaging has been recognized for classic image-based high-content screening platforms, such as the Cellomics technology, that capture information on a multitude of parameters including but not limited to cell viability, apoptosis, cell cycle, DNA repair, and adhesion (Giuliano et al., 2003; Bertelsen, 2006). Cell morphology and these other parameters are critical for full understanding of the effects of potential neurotoxicants. It is also important to recognize that low-grade contaminations can be easily visualized with phase contrast microscopy. Complete reliance on antibiotics is not always the best strategy, since there can be complex pharmacological interactions between ion channels or receptors and antibiotics (Hamilton et al., 2017).
Big data analytics in medical engineering and healthcare: methods, advances and challenges
Published in Journal of Medical Engineering & Technology, 2020
Lidong Wang, Cheryl Ann Alexander
-Omics are becoming big data-driven with various kinds of biological data including organomics, interactomics, genomics, cellomics, proteomics, in vitro, in vivo imaging, etc. Systems medicine (SM), a new methodology of medicine, has been created. SM visions a systemic view of the organism with various building elements that interplay and forms complex networks. Integrative multi-omics methods present a more comprehensive solution because various layers of disease-related data are analysed together. Some multilayer integrative methods, e.g., iCluster and multiple dataset integration (MDI) are used for several kinds of datasets; while others like CNAmet and Camelot are used for a specific combination of -omics [24]. A type of big data in life science is generated by the high throughput molecular assay. Microarray is a subset of such technology that has introduced life science to large data with high volume as well as high velocity and high variety. It studies the expression of messenger RNA (mRNA) of genes and its applications include exploring impacts of genetic mutations or medications on gene expression, monitoring cell growth under various conditions, etc. [25].