Peer Review #2 of "BioInstaller: a comprehensive R package to construct interactive and reproducible biological data analysis applications based on the R platform (v0.1)"

The increase in bioinformatics resources such as tools/scripts and databases poses a great challenge for users seeking to construct interactive and reproducible biological data analysis applications. Here, we propose an open-source, comprehensive, flexible R package named BioInstaller that consists of the R functions, Shiny application, the HTTP representational state transfer application programming interfaces, and a docker image. BioInstaller can be used to collect, manage and share various types of bioinformatics resources and perform interactive and reproducible data analyses based on the extendible Shiny application with Tom’s Obvious, Minimal Language and SQLite format databases. The source code of BioInstaller is freely available at our lab website, http://bioinfo.rjh.com.cn/labs/jhuang/tools/bioinstaller, the popular package host GitHub, https://github.com/JhuangLab/BioInstaller, and the Comprehensive R Archive Network, https://CRAN.R-project.org/package=BioInstaller. In addition, a docker image can be downloaded from DockerHub (https://hub.docker.com/r/bioinstaller/bioinstaller).

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Graph Cutting in Image Processing Handling with Biological Data Analysis

Advances in Intelligent Systems and Computing - Information Technology, Systems Research, and Computational Physics ◽

10.1007/978-3-030-18058-4_16 ◽

2019 ◽

pp. 203-216

Author(s):

Mária Ždímalová ◽

Tomáš Bohumel ◽

Katarína Plachá-Gregorovská ◽

Peter Weismann ◽

Hisham El Falougy

Keyword(s):

Image Processing ◽

Data Analysis ◽

Biological Data ◽

Biological Data Analysis

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Data-Intensive Computing Infrastructure Systems for Unmodified Biological Data Analysis Pipelines

Computational Intelligence Methods for Bioinformatics and Biostatistics - Lecture Notes in Computer Science ◽

10.1007/978-3-319-24462-4_22 ◽

2015 ◽

pp. 259-272 ◽

Cited By ~ 1

Author(s):

Lars Ailo Bongo ◽

Edvard Pedersen ◽

Martin Ernstsen

Keyword(s):

Data Analysis ◽

Biological Data ◽

Data Intensive Computing ◽

Infrastructure Systems ◽

Data Intensive ◽

Biological Data Analysis ◽

Computing Infrastructure

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Innovative Formalism for Biological Data Analysis

Encyclopedia of Information Science and Technology, Fourth Edition ◽

10.4018/978-1-5225-2255-3.ch158 ◽

2018 ◽

pp. 1814-1824

Author(s):

Calin Ciufudean

Keyword(s):

Information Technology ◽

Data Analysis ◽

Medical Devices ◽

Fuzzy Set ◽

Biological Data ◽

Fuzzy Operators ◽

Biological Data Analysis ◽

Biological Signals ◽

Design And Manufacture ◽

Gathering Data

Modern medical devices involves information technology (IT) based on electronic structures for data and signals sensing and gathering, data and signals transmission as well as data and signals processing in order to assist and help the medical staff to diagnose, cure and to monitors the evolution of patients. By focusing on biological signals processing we may notice that numerical processing of information delivered by sensors has a significant importance for a fair and optimum design and manufacture of modern medical devices. We consider for this approach fuzzy set as a formalism of analysis of biological signals processing and we propose to be accomplished this goal by developing fuzzy operators for filtering the noise of biological signals measurement. We exemplify this approach on neurological measurements performed with an Electro-Encephalograph (EEG).

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