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Comparison of Major Data Science and Machine-Learning Languages.

Journal: Biological Research for Nursing

Article Title: The State of Data Science in Genomic Nursing

doi: 10.1177/1099800420915991

Figure Lengend Snippet: Comparison of Major Data Science and Machine-Learning Languages.

Article Snippet: Other resources that provide pipeline examples include “A Tutorial on Conducting Genome-Wide Association Studies: Quality Control and Statistical Analysis” by Marees et al. (2018) , “RNA-seq Workflow: Gene-Level Exploratory Analysis and Differential Expression” by Love et al. (2019) , “A Cross-Package Bioconductor Workflow for Analyzing Methylation Array Data” by Maksimovic et al. (2019) , and “Workflow for Microbiome Data Analysis: From Raw Reads to Community Analyses” by Callahan et al. (2017) . table ft1 table-wrap mode="anchored" t5 Table 2. caption a7 Language Common Editors Popular Biological Packages Advantages Disadvantages Helpful Links R ( R Core Team, 2013 ) R RStudio Bioconductor suite Open source, popular for academic research, significant number of package implementations Memory allocation, speed, security concerns www.r-project.org rstudio.com www.bioconductor.org Python ( van Rossum, 1995 ) Jupytr Notebook Spyder PyCharm Biopython QIIME Flexible and dynamic programming, software development, extensive support libraries Speed, weak for other computing platforms python.org www.sololearn.com realpython.com Matlab ( MathWorks Inc., 2019 ) MATLAB SimBiology Deep Learning Tool Box Versatile, rich library of machine learning, and engineering libraries Cost of license, difficulties in converting code, difficult deployment of code into an application www.mathworks.com JavaScript ( Javascript, 2019 ) WebStorm Atom Editor BioJS Bionode Speed, interoperability, popular Technical support, security www.javascript.com C++ ( ISO/IEC, 2015 ) Bluefish Code: Blocks Netbeans Bio++ SeqAn NCBI toolkit Object-oriented language, application for databases Not well supported on non-Windows platforms www.cplusplus.com Scala ( Odersky, 2004 ) ENSIME Eclipse Vim BioJava Use for data analytics, reduces risk of threads Difficult recursive optimization www.scala-lang.org Julia ( Julia, 2019 ) Juno BioJulia Support for parallelism, speed, math-friendly syntax Lacking outside packages, concern about one index compared to other languages www.julialang.org Open in a separate window Comparison of Major Data Science and Machine-Learning Languages.

Techniques: Comparison, Software

Comparison of Major Data Science and Machine-Learning Languages.

Journal: Biological Research for Nursing

Article Title: The State of Data Science in Genomic Nursing

doi: 10.1177/1099800420915991

Figure Lengend Snippet: Comparison of Major Data Science and Machine-Learning Languages.

Article Snippet: Matlab ( MathWorks Inc., 2019 ) , MATLAB , SimBiology Deep Learning Tool Box , Versatile, rich library of machine learning, and engineering libraries , Cost of license, difficulties in converting code, difficult deployment of code into an application , www.mathworks.com.

Techniques: Comparison, Software