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The International Journal of the Royal Society of Thailand
Volume XI - 2019

Proteomics

Proteomics can be described as the study of proteins in tissue or body
fluids as well as their interactions and expressions in specific conditions (Ristevski
and Chen, 2018b). Nowadays more than 100,000 proteins were identified. Their

expressions can be changed according to the different pathologic conditions
(Devarajan, 2015). The proteins can be quantified by using various techniques
such as mass-spectrometry (MS)-based technologies, antibody based technologies
and aptamer-based technologies. Several biomarkers in blood and urine have
been incorporated in routine practices to trailer the therapies into individual
level. Examples of renown biomarkers are neutrophil gelatinase-associated
lipocalin (NGAL), insulin-like growth factor-binding protein 7 (IGFBP7) & Tissue
inhibitor of metalloproteinases-2 (TIMP-2) and kidney injury molecule 1 (KIM-1)
(Gunnerson et al., 2016; Wasung et al., 2015). They are originated from different
part of nephrons; therefore, they can be used as tools to identify the specific part
of nephron injury. At present, there are increasing number of newly discovered
proteins, thus, we may be able to better explain AKI pathological process in the
future (Marx et al., 2018).

Biobank

Many organizations around the world are collecting renal specimens in
order to developed biobank (2019; Calleros et al., 2012; Muruve et al., 2017; Navis
et al., 2013). Their objectives are to obtain and evaluate kidney tissues of AKI and
CKD patients in order to create a kidney tissue atlas. Their also focus on defining
disease subgroups and identify critical cell targets of novel therapies. The processes

include tissue extraction, tissue interrogation and central data library. The tissues
can be obtained by many ways including routine clinical practices, healthy
volunteers and research projects (Muruve et al., 2017). The biobank databases
serve as reference to create big data in genomic precision medicine (Figure 5).

Big data and Artificial intelligence
Big data are defined as large and complex data that are difficult to analyze

and manage with traditional software or hardware (Ristevski and Chen, 2018a).
The data consisting of texts, images and graphs can be complex (Viceconti et al.,
2015). The big data characteristics are often described as ‘6V’ including value, volume,
velocity, variety, veracity and variability. They usually contain high volume that

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