Compressive Big Data Analytics: An ensemble meta-algorithm for high-dimensional multisource datasets

Health advances are contingent on continuous development of new methods and approaches to foster data-driven discovery in the biomedical and clinical sciences. Open-science and team-based scientific discovery offer hope for tackling some of the difficult challenges associated with managing, modeling...

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Veröffentlicht in:	PloS one 2020-08, Vol.15 (8), p.e0228520, Article 0228520
Hauptverfasser:	Marino, Simeone, Zhao, Yi, Zhou, Nina, Zhou, Yiwang, Toga, Arthur W., Zhao, Lu, Jian, Yingsi, Yang, Yichen, Chen, Yehu, Wu, Qiucheng, Wild, Jessica, Cummings, Brandon, Dinov, Ivo D.
Format:	Artikel
Sprache:	eng
Schlagworte:	Age related diseases Aging Algorithms Analysis Automation Big Data Biobanks Bioinformatics Biology and Life Sciences Biomarkers Cognitive ability Computer and Information Sciences Computer applications Data analysis Data Compression Data Mining - methods Data Science - methods Datasets Empirical analysis Health behavior Heterogeneity Humans Interrogation Iterative methods Laboratories Learning algorithms Machine Learning Mathematical analysis Mathematical models Medical imaging Medical informatics Medicine Mental health Meta-Analysis as Topic Methods Models, Theoretical Mood Multidisciplinary Sciences Neuroimaging Optimization Physical Phenomena Physical Sciences Population studies Predictions Prognosis Reproducibility Reproducibility of Results Research and Analysis Methods Science & Technology Science & Technology - Other Topics Software Statistical inference Translation
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