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International Journal of Translational Medicine
Article . 2021 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Python Programming in PyPI for Translational Medicine

Authors: Yoshiyasu Takefuji;

Python Programming in PyPI for Translational Medicine

Abstract

This is the world’s first tutorial article on Python Packaging for beginners and practitioners for translational medicine or medicine in general. This tutorial will allow researchers to demonstrate and showcase their tools on PyPI packages around the world. Nowadays, for translational medicine, researchers need to deal with big data. This paper describes how to build an executable Python Package Index (PyPI) code and package. PyPI is a repository of software for the Python programming language with 5,019,737 files and 544,359 users (programmers) as of 19 October 2021. First, programmers must understand how to scrape a dataset over the Internet; second, they must read the dataset file in csv format; third, build a program to compute the target values; fourth, convert the Python program to the PyPI package.; and fifth, upload the PyPI package. This paper depicts a covidlag executable package as an example for calculating the accurate case fatality rate (CFR) and the lag time from infection to death. You can install the covidlag by pip terminal command and test it. This paper also introduces deathdaily and scorecovid packages on PyPI Stats, which can inform how many users have downloaded the specified PyPI package. The usefulness and applicability of a developed tool can be verified by PyPI Stats with the number of downloaded users.

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Keywords

PyPI package, translational medicine, Python program, Computer applications to medicine. Medical informatics, R858-859.7, dataset

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    popularity
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    Top 10%
    influence
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    impulse
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Top 10%
Average
Average
gold