Informatics and Data Analytics to Support Exposome-Based Discovery

This chapter aims at outlining the current state of science in the field of computational exposure biology and in particular at demonstrating how the bioinformatics techniques and algorithms can be used to support the association between environmental exposures and human health and the deciphering of the molecular and metabolic pathways of induced toxicity related to environmental chemical stressors. Examples of the integrated bioinformatics analyses outlined herein are given concerning exposure to airborne chemical mixtures, to organic compounds frequently found in consumer goods, and to mixtures of organic chemicals and metals through multiple exposure pathways. Advanced bioinformatics are coupled with big data analytics to perform studies of exposome-wide associations with putative adverse health outcomes. In conclusion, the chapter gives the reader an outline of the available computational tools and paves the way towards the development of future comprehensive applications that are expected to support efficiently exposome research in the 21st century.

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Informatics and Data Analytics to Support Exposome-Based Discovery

Biotechnology ◽

10.4018/978-1-5225-8903-7.ch030 ◽

2019 ◽

pp. 744-787

Author(s):

Dimosthenis A. Sarigiannis ◽

Alberto Gotti ◽

Evangelos Handakas ◽

Spyros P. Karakitsios

Keyword(s):

Big Data ◽

Health Outcomes ◽

Data Analytics ◽

Big Data Analytics ◽

Multiple Exposure ◽

Bioinformatics Analyses ◽

Environmental Chemical ◽

Exposure Pathways ◽

Adverse Health Outcomes ◽

Current State

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Big Data Analytics

University of Western Ontario Medical Journal ◽

10.5206/uwomj.v87i2.1149 ◽

2019 ◽

Vol 87 (2) ◽

pp. 24-26

Author(s):

Shawna Bourne ◽

Tarun Rihal

Keyword(s):

Big Data ◽

Health Outcomes ◽

Environmental Health ◽

Population Health ◽

Data Analytics ◽

Big Data Analytics ◽

Factors Affecting ◽

Water Test ◽

Safe Food ◽

The Impact

Utilizing big data to guide decision-making for environmental health outcomes can provide the next level of health outcome improvements on a population basis. Historical shifts in overall health and longevity came with environmental health interventions such as safe food and water supplies, the treatment of waste and the establishment of standards that have reduced acute illnesses in the population. Big data analysis approaches have the potential to have a similar impact on quality and length of life by analyzing the factors leading to chronic illness in the population, and improving outcomes. Through the use of big data and machine learning, we can learn more about the environmental factors affecting population health. This article presents an opportunity to utilize pre-existing data to explore a novel way of assessing the impact of known health hazards. This is demonstrated by using drinking water test results as a case example. We demonstrate how big data analytics can be used in such a scenario to identify environmental public health risk. This approach is beginning to be used to collect new and better organized data with the intent of improving population health outcomes.

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Big data analytics in supply chain and logistics: an empirical approach

The International Journal of Logistics Management ◽

10.1108/ijlm-05-2017-0116 ◽

2018 ◽

Vol 29 (2) ◽

pp. 767-783 ◽

Cited By ~ 26

Author(s):

Maciel Manoel Queiroz ◽

Renato Telles

Keyword(s):

Big Data ◽

Supply Chain ◽

Data Analytics ◽

Big Data Analytics ◽

Research Field ◽

Content Type ◽

Additional Tool ◽

Current State ◽

The Impact ◽

The Relationship

Purpose The purpose of this paper is to recognise the current state of big data analytics (BDA) on different organisational and supply chain management (SCM) levels in Brazilian firms. Specifically, the paper focuses on understanding BDA awareness in Brazilian firms and proposes a framework to analyse firms’ maturity in implementing BDA projects in logistics/SCM. Design/methodology/approach A survey on SCM levels of 1,000 firms was conducted via questionnaires. Of the 272 questionnaires received, 155 were considered valid, representing a 15.5 per cent response rate. Findings The knowledge of Brazilian firms regarding BDA, the difficulties and barriers to BDA project adoption, and the relationship between supply chain levels and BDA knowledge were identified. A framework was proposed for the adoption of BDA projects in SCM. Research limitations/implications This study does not offer external validity due to restrictions for the generalisation of the results even in the Brazilian context, which stems from the conducted sampling. Future studies should improve the comprehension in this research field and focus on the impact of big data on supply chains or networks in emerging world regions, such as Latin America. Practical implications This paper provides insights for practitioners to develop activities involving big data and SCM, and proposes functional and consistent guidance through the BDA-SCM triangle framework as an additional tool in the implementation of BDA projects in the SCM context. Originality/value This study is the first to analyse BDA on different organisational and SCM levels in emerging countries, offering instrumentalisation for BDA-SCM projects.

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Big Data Analytics: The Next Big Thing

The Management Accountant Journal ◽

10.33516/maj.v54i5.20-24p ◽

2019 ◽

Vol 54 (5) ◽

pp. 20

Author(s):

Dheeraj Kumar Pradhan

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics

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Strategy in Discovery Mode - Wie Big Data & Analytics strategisches Denken verdrängen kann

WiSt - Wirtschaftswissenschaftliches Studium ◽

10.15358/0340-1650-2020-5-11 ◽

2020 ◽

Vol 49 (5) ◽

pp. 11-17

Author(s):

Thomas Wrona ◽

Pauline Reinecke

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.

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