Using Big Data to Discover Diagnostics and Therapeutics for Gastrointestinal and Liver Diseases
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TLDR
Recent advances in the fields of computational and systems biology are reviewed and opportunities for researchers to use big data sets in the field of gastroenterology and hepatology to complement traditional means of diagnostic and therapeutic discovery are highlighted.About:
This article is published in Gastroenterology.The article was published on 2017-01-01 and is currently open access. It has received 50 citations till now. The article focuses on the topics: Translational bioinformatics.read more
Citations
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Mechanisms of NAFLD development and therapeutic strategies
TL;DR: Understanding of pathogenic mechanisms and clinical features of NAFLD is driving progress in therapeutic strategies now in clinical trials and the emerging targets for drug development that involve either single agents or combination therapies intended to arrest or reverse disease progression are discussed.
Journal ArticleDOI
Mechanisms and disease consequences of nonalcoholic fatty liver disease
TL;DR: In this paper, the authors provide an in-depth discussion of the underlying pathogenetic mechanisms that lead to progressive liver injury, including the metabolic origins of NAS, the effect of NAFLD on hepatic glucose and lipid metabolism, bile acid toxicity, macrophage dysfunction, and hepatic stellate cell activation, and consider the role of genetic, epigenetic and environmental factors that promote fibrosis progression and risk of hepatocellular carcinoma in NASH.
Journal ArticleDOI
Big Data’s Role in Precision Public Health
TL;DR: This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in Precision public health efforts.
Journal ArticleDOI
Limitations of non-invasive tests for assessment of liver fibrosis.
Keyur Patel,Giada Sebastiani +1 more
TL;DR: Understanding the strengths and limitations of these non-invasive tests will allow for more judicious interpretation in the clinical context, where NITs should be viewed as complementary to, rather than as a replacement for, liver biopsy.
Detecting Novel Associations in Large Data Sets
David N. Reshef,David N. Reshef,David N. Reshef,Yakir A. Reshef,Yakir A. Reshef,Hilary K. Finucane,Sharon R. Grossman,Sharon R. Grossman,Gilean McVean,Gilean McVean,Peter J. Turnbaugh,Eric S. Lander,Eric S. Lander,Eric S. Lander,Michael Mitzenmacher,Pardis C. Sabeti,Pardis C. Sabeti +16 more
TL;DR: The maximal information coefficient (MIC) as mentioned in this paper is a measure of dependence for two-variable relationships that captures a wide range of associations both functional and not, and for functional relationships provides a score that roughly equals the coefficient of determination (R2) of the data relative to the regression function.
References
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Tanya Barrett,Stephen E. Wilhite,Pierre Ledoux,Carlos Evangelista,Irene F. Kim,Maxim Tomashevsky,Kimberly A. Marshall,Katherine Phillippy,Patti M. Sherman,Michelle Holko,Andrey Yefanov,Hye Seung Lee,Naigong Zhang,Cynthia L. Robertson,Nadezhda Serova,Sean Davis,Alexandra Soboleva +16 more
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