Biological Big Data Analytics

Gharajeh, Mohammad Samadi

doi:10.1016/bs.adcom.2017.08.002

Cited by 19 publications

(10 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e literature mostly highlights the importance, challenges, and benefits of BDA in healthcare organizations [3,19,20]. Only a few studies discussed the adoption factors at a very basic level [4,21,22].…”

Section: Complexitymentioning

confidence: 99%

Moderating Effects of Gender and Resistance to Change on the Adoption of Big Data Analytics in Healthcare

et al. 2020

View full text Add to dashboard Cite

The big data analytics (BDA) has dragged tremendous attention in healthcare organizations. Healthcare organizations are investing substantial money and time in big data analytics and want to adopt it to get potential benefits. Thus, this study proposes a BDA adoption model in healthcare organizations to explore the critical factors that can influence its adoption process. The study extends the technology acceptance model (TAM) with the self-efficacy as an external factor and also includes gender and resistance to change (RTC) as moderators to strengthen the research model. The proposed research model has been tested on 283 valid responses which were collected through a structured survey, by applying structural equation modeling. Our results portray that self-efficacy is a strong predictor of intention to use BDA along with other TAM factors. Moreover, it is confirmed by the results that RTC dampens the positive relationship between intention to use and actual use of BDA in healthcare organizations. The outcomes revealed that male employees as compared to female employees are dominant towards the positive intention to use BDA. Furthermore, females create more RTC than males while adopting BDA in healthcare organizations. Theoretical and practical implications, limitations, and future research directions also underlined in this study.

show abstract

Section: Complexitymentioning

confidence: 99%

Moderating Effects of Gender and Resistance to Change on the Adoption of Big Data Analytics in Healthcare

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Therefore, describing the crucial factors that are required for understanding is important prior to creating a strategy for the acceptance of BDA in the healthcare industry, particularly in developing countries such as Pakistan, where the industry requires filling the gap of BDA adoption. Furthermore, data (i.e., big data) related to healthcare are generated at a very high pace [9], and existing systems are unable to store and analyze the huge volume, velocity and variety of data [10]. Therefore, a need exists for a system with the ability to store and analyze data with high volumes, velocities, and variety, all of which are provided by BDA systems [9].…”

mentioning

confidence: 99%

Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change

et al. 2019

View full text Add to dashboard Cite

IntroductionBig data analytics (BDA) is a course of action to examine large and complex data sets (i.e., big data) and select veiled information that can help organizations with efficient decision making [1]. The volume of data related to healthcare organizations has grown dramatically in past years and is expected to increase in coming years due to the use of innovative technologies [2]. Meanwhile, healthcare reimbursement methods are changing, and pay for performance is an emerging factor in the current healthcare environment. Recently, healthcare organizations have only focused on profit and have neglected to acquire the essential tools, infrastructure, and technologies for effective control of big data to ensure citizens' health care [3,4]. Big data incorporates features such as variety, velocity, and veracity. BDA techniques can be applied to the massive amount of prevailing patient-related medical information to analyze outcomes for improvement of the healthcare sector [5,6]. Using BDA in the healthcare sector will help inform each physician of the medical histories of individuals and the population and enable appropriate Abstract Big data analytics is gaining substantial attention due to its innovative contribution to decision making and strategic development across the healthcare field. Therefore, this study explored the adoption mechanism of big data analytics in healthcare organizations to inspect elements correlated to behavioral intention using the technology acceptance model and task-technology fit paradigm. Using a survey questionnaire, we analyzed 224 valid responses in AMOS v21 to test the hypotheses. Our results posit that the credentials of the technology acceptance model together with task-technology fit contribute substantially to the enhancement of behavioral intentions to use the big data analytics system in healthcare, ultimately leading towards actual use. Meanwhile, trust in and security of the information system also positively influenced the behavioral intention for use. Employee resistance to change is a key factor underlying failure of the innovative system in organizations and has been proven in this study to negatively moderate the relationship between intention to use and actual use of big data analytics in healthcare. Our results can be implemented by healthcare organizations to develop an understanding of the implementation of big data analytics and to promote psychological empowerment of employees to accept this innovative system. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

show abstract

“…To help matters, there has been some promising advances that have been recently introduced into plant science. “Big data” analytics are increasingly being used to discover hidden patterns, correlations and other insights from fragmented studies (Gharajeh, 2018). Additionally, genome-wide gene expression profiling and other “omics” technologies are indeed needed, started with proteomics approaches which are now becoming widely used for studying various aspects of plant death.…”

Section: Resultsmentioning

confidence: 99%

Cutting Out the Gaps Between Proteases and Programmed Cell Death

Balakireva

Zamyatnin

2019

Front. Plant Sci.

View full text Add to dashboard Cite

To date, many animal models for programmed cell death (PCD) have been extensively characterized and classified while such efforts in plant types of PCD still remain poorly understood. However, despite a wide range of functional differences between PCD types in animals and plants, it is certain that all of them are regulated through the recruitment of proteases. Most importantly, proteases are able to perform proteolysis that results in a gain or loss of protein function. This principle relies on the presence of proteolytic cascades where proteases are activated upon various upstream stimuli and which lead to repetitive cell death. While protease activation, proteolytic cascades and targeted substrates are described in detail mainly for nematode, human, and mice models of apoptosis, for plants, only fragmentary knowledge of protease involvement in PCD exists. However, recently, data on the regulation of general plant PCD and protease involvement have emerged which deepens our understanding of the molecular mechanisms responsible for PCD in plants. With this in mind, this article highlights major aspects of protease involvement in the execution of PCD in both animals and plants, addresses obstacles and advances in the field and proposes recommendations for further research of plant PCD.

show abstract

Biological Big Data Analytics

Cited by 19 publications

References 20 publications

Moderating Effects of Gender and Resistance to Change on the Adoption of Big Data Analytics in Healthcare

Moderating Effects of Gender and Resistance to Change on the Adoption of Big Data Analytics in Healthcare

Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change

Cutting Out the Gaps Between Proteases and Programmed Cell Death

Contact Info

Product

Resources

About