Bibliometric Analysis using Cytochrome P450 (CYP51) Data Between 1980–2018 for BigData and Blockchain in Biomedical Research

Information sciences is a wide field of study using information in the form of data or text which can be arranged or categorized using pattern recognition whether mathematically via algorithms or by subject matter to better understand a topic of interest. Here this study uses Cytochrome P450 enzyme, a sterol demethylase (CYP51) publication data as the subject matter to understand collaboration between 1980 and 2018. This paves the way for bigdata and blockchain in biomedical research. Sterol demethylase CYP51 published journal articles includes research articles, reviews, technical notes and reports related to all kingdoms of life. This study uses priori thematic analysis method of CYP51 published literature focusing on authors, geographical areas and selected kingdoms of life. Purposive sampling method was used to collect the data from scientific databases. Data source triangulation was employed involving different data search to gain multiple validation of results. It was found 504 published articles have met the inclusion criteria. Overall, this study showed that a total of 2454 authors were involved in CYP51 research and majority are located in North America region. This bibliographic analysis (BA) outlined rough case-study approach on data searching options for organisms which may be expanded into studies of wider kingdoms, regions and organisms using data analytics algorithms advances.