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big data analytics

Michael P Wilmot, Nick Haslam, Jingyuan Tian, Deniz S Ones
We present direct and conceptual replications of the influential taxometric analysis of Type A Behavior (TAB; Strube, 1989), which reported evidence for the latent typology of the construct. Study 1, the direct replication (N = 2,373), duplicated sampling and methodological procedures of the original study, but results showed that the item indicators used in the original study lacked sufficient validity to unambiguously determine latent structure. Using improved factorial subscale indicators to further test the question, multiple taxometric procedures, in combination with parallel analyses of simulated data, failed to replicate the original typological finding...
May 17, 2018: Journal of Personality and Social Psychology
Bashar Ibrahim, Dino P McMahon, Franziska Hufsky, Martin Beer, Li Deng, Philippe Le Mercier, Massimo Palmarini, Volker Thiel, Manja Marz
Despite the recognized excellence of virology and bioinformatics, these two communities have interacted surprisingly sporadically, aside from some pioneering work on HIV-1 and influenza. Bringing together the expertise of bioinformaticians and virologists is crucial, since very specific but fundamental computational approaches are required for virus research, particularly in an era of big data. Collaboration between virologists and bioinformaticians is necessary to improve existing analytical tools, cloud-based systems, computational resources, data sharing approaches, new diagnostic tools, and bioinformatic training...
May 8, 2018: Virus Research
E Allen Foegeding
No abstract text is available yet for this article.
May 2018: Journal of Food Science
Blagoj Ristevski, Ming Chen
This paper surveys big data with highlighting the big data analytics in medicine and healthcare. Big data characteristics: value, volume, velocity, variety, veracity and variability are described. Big data analytics in medicine and healthcare covers integration and analysis of large amount of complex heterogeneous data such as various - omics data (genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenomics, diseasomics), biomedical data and electronic health records data...
May 10, 2018: Journal of Integrative Bioinformatics
Jasmine Rossi-deVries, Valentina Pedoia, Michael A Samaan, Adam R Ferguson, Richard B Souza, Sharmila Majumdar
BACKGROUND: Osteoarthritis (OA) is a multifaceted disease with many variables affecting diagnosis and progression. Topological data analysis (TDA) is a state-of-the-art big data analytics tool that can combine all variables into multidimensional space. TDA is used to simultaneously analyze imaging and gait analysis techniques. PURPOSE: To identify biochemical and biomechanical biomarkers able to classify different disease progression phenotypes in subjects with and without radiographic signs of hip OA...
May 7, 2018: Journal of Magnetic Resonance Imaging: JMRI
Todd R McNutt, Stanley H Benedict, Daniel A Low, Kevin Moore, Ilya Shpitser, Wei Jiang, Pranav Lakshminarayanan, Zhi Cheng, Peijin Han, Xuan Hui, Minoru Nakatsugawa, Junghoon Lee, Joseph A Moore, Scott P Robertson, Veeraj Shah, Russ Taylor, Harry Quon, John Wong, Theodore DeWeese
Big clinical data analytics as a primary component of precision medicine is discussed, identifying where these emerging tools fit in the spectrum of genomics and radiomics research. A learning health system (LHS) is conceptualized that uses clinically acquired data with machine learning to advance the initiatives of precision medicine. The LHS is comprehensive and can be used for clinical decision support, discovery, and hypothesis derivation. These developing uses can positively impact the ultimate management and therapeutic course for patients...
June 1, 2018: International Journal of Radiation Oncology, Biology, Physics
Giovanni Iacono, Elisabetta Mereu, Amy Guillaumet-Adkins, Roser Corominas, Ivon Cuscó, Gustavo Rodríguez-Esteban, Marta Gut, Luis Alberto Pérez-Jurado, Ivo Gut, Holger Heyn
Single-cell RNA sequencing (scRNA-seq) has significantly deepened our insights into complex tissues, with the latest techniques capable of processing tens of thousands of cells simultaneously. Analyzing increasing numbers of cells, however, generates extremely large data sets, extending processing time and challenging computing resources. Current scRNA-seq analysis tools are not designed to interrogate large data sets and often lack sensitivity to identify marker genes. With bigSCale, we provide a scalable analytical framework to analyze millions of cells, which addresses the challenges associated with large data sets...
May 3, 2018: Genome Research
Xinbin Dai, Zhaohong Zhuang, Patrick Xuechun Zhao
Plant regulatory small RNAs (sRNAs), which include most microRNAs (miRNAs) and a subset of small interfering RNAs (siRNAs), such as the phased siRNAs (phasiRNAs), play important roles in regulating gene expression. Although generated from genetically distinct biogenesis pathways, these regulatory sRNAs share the same mechanisms for post-translational gene silencing and translational inhibition. psRNATarget was developed to identify plant sRNA targets by (i) analyzing complementary matching between the sRNA sequence and target mRNA sequence using a predefined scoring schema and (ii) by evaluating target site accessibility...
April 30, 2018: Nucleic Acids Research
Jonathan P Caulkins, Yilun Bao, Steve Davenport, Imane Fahli, Yutian Guo, Krista Kinnard, Mary Najewicz, Lauren Renaud, Beau Kilmer
INTRODUCTION: Voters in eight U.S. states have passed initiatives to legalize large-scale commercial production of cannabis for non-medical use. All plan or require some form of "seed-to-sale" tracking systems, which provide a view of cannabis market activity at a heretofore unimagined level of detail. Legal markets also create a range of new matters for policy makers to address. DATA: Publicly available data were obtained on approximately 45 million individually priced items purchased in the 35 million retail transactions that took place during the first two and a half years of Washington State's legal cannabis market...
April 27, 2018: International Journal on Drug Policy
Saeedeh Mohammadi, Hadi Parastar
In this work, a chemometrics-based strategy is developed for quantitative mass spectrometry imaging (MSI). In this regard, quantification of chlordecone as a carcinogenic organochlorinated pesticide (C10Cll0O) in mouse liver using the matrix-assisted laser desorption ionization MSI (MALDI-MSI) method is used as a case study. The MSI datasets corresponded to 1, 5 and 10 days of mouse exposure to the standard chlordecone in the quantity range of 0 to 450 μg g-1. The binning approach in the m/z direction is used to group high resolution m/z values and to reduce the big data size...
April 30, 2018: Analyst
Ashish Kumar Kakkar, Harmanjit Singh, Bikash Medhi
Parkinson's disease (PD) is a chronic progressive neurological disorder characterized by accumulation of Lewy bodies and profound loss of substantia nigra dopaminergic neurons. PD symptomatology is now recognized to include both cardinal motor as well as clinically significant non-motor symptoms. Despite intensive research, the current understanding of molecular mechanisms underlying neurodegeneration in PD is limited and has hampered the development of novel symptomatic and disease modifying therapies. The currently available treatment options are only partially or transiently effective and fail to restore the lost dopaminergic neurons or retard disease progression...
April 21, 2018: European Journal of Pharmacology
Kwok-Chan Lun
Health informatics has benefitted from the development of Info-Communications Technology (ICT) over the last fifty years. Advances in ICT in healthcare have now started to spur advances in Data Technology as hospital information systems, electronic health and medical records, mobile devices, social media and Internet Of Things (IOT) are making a substantial impact on the generation of data. It is timely for healthcare institutions to recognize data as a corporate asset and promote a data-driven culture within the institution...
April 22, 2018: Yearbook of Medical Informatics
Alexander S Kirpich, Miguel Ibarra, Oleksandr Moskalenko, Justin M Fear, Joseph Gerken, Xinlei Mi, Ali Ashrafi, Alison M Morse, Lauren M McIntyre
BACKGROUND: Metabolomics has the promise to transform the area of personalized medicine with the rapid development of high throughput technology for untargeted analysis of metabolites. Open access, easy to use, analytic tools that are broadly accessible to the biological community need to be developed. While technology used in metabolomics varies, most metabolomics studies have a set of features identified. Galaxy is an open access platform that enables scientists at all levels to interact with big data...
April 20, 2018: BMC Bioinformatics
Johan van Soest, Chang Sun, Ole Mussmann, Marco Puts, Bob van den Berg, Alexander Malic, Claudia van Oppen, David Towend, Andre Dekker, Michel Dumontier
Conventional data mining algorithms are unable to satisfy the current requirements on analyzing big data in some fields such as medicine, policy making, judicial, and tax records. However, applying diverse datasets from different institutes (both healthcare and non-healthcare related) can enrich information and insights. So far, analyzing this data in an automated, privacy-preserving manner does not exist to our knowledge. In this work, we propose an infrastructure, and proof-of-concept for privacy-preserving analytics on vertically partitioned data...
2018: Studies in Health Technology and Informatics
Nishita Mehta, Anil Pandit
BACKGROUND: The application of Big Data analytics in healthcare has immense potential for improving the quality of care, reducing waste and error, and reducing the cost of care. PURPOSE: This systematic review of literature aims to determine the scope of Big Data analytics in healthcare including its applications and challenges in its adoption in healthcare. It also intends to identify the strategies to overcome the challenges. DATA SOURCES: A systematic search of the articles was carried out on five major scientific databases: ScienceDirect, PubMed, Emerald, IEEE Xplore and Taylor & Francis...
June 2018: International Journal of Medical Informatics
Angkoon Phinyomark, Giovanni Petri, Esther Ibáñez-Marcelo, Sean T Osis, Reed Ferber
The increasing amount of data in biomechanics research has greatly increased the importance of developing advanced multivariate analysis and machine learning techniques, which are better able to handle "big data". 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. This paper begins with a brief introduction to an automated three-dimensional (3D) biomechanical gait data collection system: 3D GAIT, followed by how the studies in the field of gait biomechanics fit the quantities in the 5 V's definition of big data: volume, velocity, variety, veracity, and value...
2018: Journal of Medical and Biological Engineering
B J White, D E Amrine, R L Larson
Big data are frequently used in many facets of business and agronomy to enhance knowledge needed to improve operational decisions. Livestock operations collect data of sufficient quantity to perform predictive analytics. Predictive analytics can be defined as a methodology and suite of data evaluation techniques to generate a prediction for specific target outcomes. The objective of this manuscript is to describe the process of using big data and the predictive analytic framework to create tools to drive decisions in livestock production, health, and welfare...
April 14, 2018: Journal of Animal Science
Nishith Kumar, Md Aminul Hoque, Masahiro Sugimoto
BACKGROUND: The identification of differential metabolites in metabolomics is still a big challenge and plays a prominent role in metabolomics data analyses. Metabolomics datasets often contain outliers because of analytical, experimental, and biological ambiguity, but the currently available differential metabolite identification techniques are sensitive to outliers. RESULTS: We propose a kernel weight based outlier-robust volcano plot for identifying differential metabolites from noisy metabolomics datasets...
April 11, 2018: BMC Bioinformatics
Ching-Yen Kuo, Ren-Hao Pan, Chin-Kan Chan, Chiung-Yi Wu, Dinh-Van Phan, Chien-Lung Chan
Few studies have assessed the lagged effects of levels of different urban city air pollutants and seasons on asthma hospitalization in children. This study used big data analysis to explore the effects of daily changes in air pollution and season on childhood asthma hospitalization from 2001 to 2010 in Taipei and Kaohsiung City, Taiwan. A time-stratified case-crossover study and conditional logistic regression analysis were employed to identify associations between the risk of hospitalization due to asthma in children and the levels of air pollutants (PM2...
March 31, 2018: International Journal of Environmental Research and Public Health
Manuel Rodríguez-Martínez
Social media has become an important platform to gauge public opinion on topics related to our daily lives. In practice, processing these posts requires big data analytics tools since the volume of data and the speed of production overwhelm single-server solutions. Building an application to capture and analyze posts from social media can be a challenge simply because it requires combining a set of complex software tools that often times are tricky to configure, tune, and maintain. In many instances, the application ends up being an assorted collection of Java/Scala programs or Python scripts that developers cobble together to generate the data products they need...
June 2017: Proceedings. IEEE International Congress on Big Data
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