Faculty Opinions recommendation of Big Data from Pharmaceutical Patents: A Computational Analysis of Medicinal Chemists' Bread and Butter.

Author(s):  
John Lowe
2016 ◽  
Vol 59 (9) ◽  
pp. 4385-4402 ◽  
Author(s):  
Nadine Schneider ◽  
Daniel M. Lowe ◽  
Roger A. Sayle ◽  
Michael A. Tarselli ◽  
Gregory A. Landrum

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Luís Fernando Sayão ◽  
Luana Farias Sales

RESUMO A ciência contemporânea e seus fundamentos metodológicos têm sido impactados pelo fenômeno do big data, que proclama que na era dos dados medidos em petabytes, de supercomputadores e sofisticados algoritmos, o método científico está obsoleto e que as hipóteses e modelos estão superados. As estratégias do big data científico confia em estratégias de análises computacionais de massivas quantidades de dados para revelar correlações, padrões e regras que vão gerar novos conhecimentos, que vão das ciências exatas até as ciências sociais, humanidade e cultura, delineando um arquétipo de ciência orientada por dados. O presente ensaio coloca em pauta as controvérsias em torno da ciência orientada por dados em contraposição à ciência orientada por hipóteses, e analisa alguns dos desdobramentos desse embate epistemológico. Para tal, tomo como metodologia os escritos de alguns autores mais proximamente envolvidos nessa questão.Palavras-chave: Big Data; Método Cientifico; Ciência Orientada por Dados; Ciência Orientada por Hipóteses.ABSTRACT Contemporary science and its methodological foundations have been impacted by the big data phenomenon that proclaims that in the age of data measured in petabytes, supercomputers and sophisticated algorithms the scientific method is obsolete and that the hypotheses and models are outdated.The strategies of the scientific big data rely on computational analysis strategies of massive amounts of data to reveal correlations, patterns and rules that will generate new knowledge, ranging from the exact sciences to the social sciences, humanity and culture, outlining an archetype of data-driven science. The present essay addresses the debates around data-driven science as opposed to hypothesis-oriented science and analyzes some of the ramifications of this epistemological confrontation. For this, the writings of some authors who are more closely involved in this question are taken as methodology.Keywords: Big Data; Scientific Method; Data-Driven Science; Hypothesis-Driven Science.


2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Guang Lan Zhang ◽  
Jing Sun ◽  
Lou Chitkushev ◽  
Vladimir Brusic

With the vast amount of immunological data available, immunology research is entering the big data era. These data vary in granularity, quality, and complexity and are stored in various formats, including publications, technical reports, and databases. The challenge is to make the transition from data to actionable knowledge and wisdom and bridge the knowledge gap and application gap. We report a knowledge-based approach based on a framework called KB-builder that facilitates data mining by enabling fast development and deployment of web-accessible immunological data knowledge warehouses. Immunological knowledge discovery relies heavily on both the availability of accurate, up-to-date, and well-organized data and the proper analytics tools. We propose the use of knowledge-based approaches by developing knowledgebases combining well-annotated data with specialized analytical tools and integrating them into analytical workflow. A set of well-defined workflow types with rich summarization and visualization capacity facilitates the transformation from data to critical information and knowledge. By using KB-builder, we enabled streamlining of normally time-consuming processes of database development. The knowledgebases built using KB-builder will speed up rational vaccine design by providing accurate and well-annotated data coupled with tailored computational analysis tools and workflow.


2015 ◽  
Vol 2 (1) ◽  
pp. 12-18 ◽  
Author(s):  
Yiming Qin ◽  
Hari Krishna Yalamanchili ◽  
Jing Qin ◽  
Bin Yan ◽  
Junwen Wang

ASHA Leader ◽  
2013 ◽  
Vol 18 (2) ◽  
pp. 59-59
Keyword(s):  

Find Out About 'Big Data' to Track Outcomes


2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.


2017 ◽  
Vol 225 (3) ◽  
pp. 287-288
Keyword(s):  

An associated conference will take place at ZPID – Leibniz Institute for Psychology Information in Trier, Germany, on June 7–9, 2018. For further details, see: http://bigdata2018.leibniz-psychology.org


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