56,232 research outputs found
A Microsoft-Excel-based tool for running and critically appraising network meta-analyses--an overview and application of NetMetaXL.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.BACKGROUND: The use of network meta-analysis has increased dramatically in recent years. WinBUGS, a freely available Bayesian software package, has been the most widely used software package to conduct network meta-analyses. However, the learning curve for WinBUGS can be daunting, especially for new users. Furthermore, critical appraisal of network meta-analyses conducted in WinBUGS can be challenging given its limited data manipulation capabilities and the fact that generation of graphical output from network meta-analyses often relies on different software packages than the analyses themselves. METHODS: We developed a freely available Microsoft-Excel-based tool called NetMetaXL, programmed in Visual Basic for Applications, which provides an interface for conducting a Bayesian network meta-analysis using WinBUGS from within Microsoft Excel. . This tool allows the user to easily prepare and enter data, set model assumptions, and run the network meta-analysis, with results being automatically displayed in an Excel spreadsheet. It also contains macros that use NetMetaXL's interface to generate evidence network diagrams, forest plots, league tables of pairwise comparisons, probability plots (rankograms), and inconsistency plots within Microsoft Excel. All figures generated are publication quality, thereby increasing the efficiency of knowledge transfer and manuscript preparation. RESULTS: We demonstrate the application of NetMetaXL using data from a network meta-analysis published previously which compares combined resynchronization and implantable defibrillator therapy in left ventricular dysfunction. We replicate results from the previous publication while demonstrating result summaries generated by the software. CONCLUSIONS: Use of the freely available NetMetaXL successfully demonstrated its ability to make running network meta-analyses more accessible to novice WinBUGS users by allowing analyses to be conducted entirely within Microsoft Excel. NetMetaXL also allows for more efficient and transparent critical appraisal of network meta-analyses, enhanced standardization of reporting, and integration with health economic evaluations which are frequently Excel-based.CC is a recipient of a Vanier Canada Graduate Scholarship from the Canadian Institutes of Health Research (funding reference number—CGV 121171) and is a trainee on the Canadian Institutes of Health Research Drug Safety and Effectiveness Network team grant (funding reference number—116573). BH is funded by a New Investigator award from the Canadian Institutes of Health Research and the Drug Safety and Effectiveness Network. This research was partly supported by funding from CADTH as part of a project to develop Excel-based tools to support the conduct of health technology assessments. This research was also supported by Cornerstone Research Group
Random matrix analysis for gene interaction networks in cancer cells
Investigations of topological uniqueness of gene interaction networks in
cancer cells are essential for understanding this disease. Based on the random
matrix theory, we study the distribution of the nearest neighbor level spacings
of interaction matrices for gene networks in human cancer cells. The
interaction matrices are computed using the Cancer Network Galaxy (TCNG)
database, which is a repository of gene interactions inferred by a Bayesian
network model. 256 NCBI GEO entries regarding gene expressions in human cancer
cells have been selected for the Bayesian network calculations in TCNG. We
observe the Wigner distribution of when the gene networks are dense
networks that have more than edges. In the opposite case, when
the networks have smaller numbers of edges, the distribution becomes the
Poisson distribution. We investigate relevance of both to the size of
the networks and to edge frequencies that manifest reliance of the inferred
gene interactions.Comment: 22 pages, 7 figure
Teaching Data Science
We describe an introductory data science course, entitled Introduction to
Data Science, offered at the University of Illinois at Urbana-Champaign. The
course introduced general programming concepts by using the Python programming
language with an emphasis on data preparation, processing, and presentation.
The course had no prerequisites, and students were not expected to have any
programming experience. This introductory course was designed to cover a wide
range of topics, from the nature of data, to storage, to visualization, to
probability and statistical analysis, to cloud and high performance computing,
without becoming overly focused on any one subject. We conclude this article
with a discussion of lessons learned and our plans to develop new data science
courses.Comment: 10 pages, 4 figures, International Conference on Computational
Science (ICCS 2016
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