“…In the last decade, new disciplines like computational social science and social data science have created methods to collect and analyse such data that have considerably expanded the scope of social science research. …”
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“…In this study, we aim to apply a data science approach to a large dataset of smartwatch ECGs in order to deliver an improved algorithm. …”
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“…The review is divided into six sections: (1) compendia of data resources; (2) biomolecular repository “Hubs”; (3) a list of cancer-related data resources, which provides information on contents of the resource and whether the resource enables upload and analysis of investigator provided data; (4) a list of seminal publications containing specific breast cancer data, e.g., publications from METBRIC, Sanger, TCGA; (5) a list of journals focused on data science that include cancer-related “Big Data”; and (6) miscellaneous resources.…”
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“…There is a pressing need for cross-disciplinary research with an integrative approach to data science, whereby basic scientists, clinicians, data analysts, and epidemiologists work together to understand the heterogeneity of asthma.…”
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“…Cancer transcriptome analysis is one of the leading areas of Big Data science, biomarker, and pharmaceutical discovery, not to forget personalized medicine. …”
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“…Our study presents a new perspective on incorporating gene coexpression networks into the gene expression signature and clinical application of deep learning in genomic data science for prognosis prediction.…”
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“…Consequently, advances in data science methods will expand the knowledge for testing new hypotheses about biomechanical risk factors associated with walking and running gait-related musculoskeletal injury. …”
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“…Exploring and detecting the causal relations among variables have shown huge practical values in recent years, with numerous opportunities for scientific discovery, and have been commonly seen as the core of data science. Among all possible causal discovery methods, causal discovery based on a constraint approach could recover the causal structures from passive observational data in general cases, and had shown extensive prospects in numerous real world applications. …”
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“…New training that integrates domain knowledge in biodiversity and data science skills is also needed to accelerate research in these areas. …”
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“…Biomedical informatics is a multidisciplinary field that falls at the intersection of information technology, computer and data science, engineering, and healthcare that will be instrumental for uncovering novel insights into neurodegenerative disease research, including both causal relationships and therapeutic targets and maximizing the utility of both clinical and research data. …”
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“…Despite ongoing challenges, advances in data science and the resources and expertise technology companies can contribute will be a key factor in ensuring that the Internet helps end and not fuel the public health emergency of opioid abuse.…”
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“…The importance of bioinformatics, computational biology, and data science in biomedical research continues to grow, driving a need for effective instruction and education. …”
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“…This paper is an extension of a communication presented in the Process-Oriented Data Science for Health Workshop in the Business Process Management Conference 2018.…”
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“…After a change and update in 2016, the NHIRD is maintained and regulated by the Data Science Centre of the Ministry of Health and Welfare of Taiwan. …”
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“…These approaches include: improved applications of quantitative methods to generate insightful epidemiological descriptions of outbreaks; robust investigations of causal agents and risk factors; tools to assess response needs; identifying and monitoring optimal interventions or combinations of interventions; and forecasting for response planning. Data science and quantitative approaches can improve decision-making in outbreak response. …”
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“…The response to this opportunity has included the development of open databases for research and other purposes; the development of a collaborative form of clinical data science intended to fully leverage these data resources, and the creation of data-driven applications for purposes such as clinical decision support. …”
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“…There has been a growing recognition of the opportunities afforded by advanced data science and informatics approaches in addressing the computational demands of modeling and simulation of multiscale materials science phenomena. …”
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“…In data science, determining proximity between observations is critical to many downstream analyses such as clustering, classification, and prediction. …”
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“…We provide this collective commentary that synthesizes participant feedback regarding critical challenges that prohibit the scale and spread of ePROs across healthcare delivery systems, including governance and leadership, workflow and human factors, informatics, and data science. In order to realize the promise of ePROs at scale, adaptable approaches are critical to balance the needs of individual users with health systems at large.…”
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“…As an example, we propose that the application of machine learning and data science to corpora of biomedical literature and anonymized clinical data might allow us to see if there are objective “signatures” of research uniquely enabled by MD/PhD training. …”
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