Data and Text Processing for Health and Life Sciences

an example driven introductory guide using shell scripting

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Last edited by MARC Bot
November 16, 2020 | History

Data and Text Processing for Health and Life Sciences

an example driven introductory guide using shell scripting

  • 0 Ratings
  • 4 Want to read
  • 0 Currently reading
  • 0 Have read

This open access book is a step-by-step introduction on how shell scripting can help solve many of the data processing tasks that Health and Life specialists face everyday with minimal software dependencies. The examples presented in the book show how simple command line tools can be used and combined to retrieve data and text from web resources, to filter and mine literature, and to explore the semantics encoded in biomedical ontologies. To store data this book relies on open standard text file formats, such as TSV, CSV, XML, and OWL, that can be open by any text editor or spreadsheet application.
The first two chapters, Introduction and Resources, provide a brief introduction to the shell scripting and describe popular data resources in Health and Life Sciences. The third chapter, Data Retrieval, starts by introducing a common data processing task that involves multiple data resources. Then, this chapter explains how to automate each step of that task by introducing the required commands line tools one by one. The fourth chapter, Text Processing, shows how to filter and analyze text by using simple string matching techniques and regular expressions. The last chapter, Semantic Processing, shows how XPath queries and shell scripting is able to process complex data, such as the graphs used to specify ontologies.
Besides being almost immutable for more than four decades and being available in most of our personal computers, shell scripting is relatively easy to learn by Health and Life specialists as a sequence of independent commands. Comprehending them is like conducting a new laboratory protocol by testing and understanding its procedural steps and variables, and combining their intermediate results. Thus, this book is particularly relevant to Health and Life specialists or students that want to easily learn how to process data and text, and which in return may facilitate and inspire them to acquire deeper bioinformatics skills in the future.

Publish Date
Publisher
Springer
Language
English
Pages
107

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Previews available in: English

Edition Availability
Cover of: Data and Text Processing for Health and Life Sciences
Data and Text Processing for Health and Life Sciences
Oct 09, 2020, Saint Philip Street Press
paperback
Cover of: Data and Text Processing for Health and Life Sciences

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Book Details


Published in

Switzerland

Table of Contents

Chapter 1. Introduction
Chapter 2. Resources
Chapter 3. Data Retrieval
Chapter 4. Text Processing
Chapter 5. Semantic Processing

Edition Notes

Series
Advances in Experimental Medicine and Biology
Copyright Date
2019

The Physical Object

Format
eBook
Number of pages
107

ID Numbers

Open Library
OL27005115M
Internet Archive
DataAndTextProcessingForHealthAndLifeSciences
ISBN 13
9783030138455
Amazon ID (ASIN)
Processing-Sciences-Advances-Experimental-Medicine/dp/3030138445/
Google
7cmcDwAAQBAJ

Excerpts

In summary, this book is directed mainly towards Health and Life specialists and students that need to know how to process biomedical data and text, without being dependent on continuous financial support, third-party applications, or advanced computer skills.
Page 5, added by fcouto. "Why This Book?"
To address the issue of ambiguity of natural language and contextualization of the message, text processing techniques can explore current biomedical ontologies.
Page 2, added by fcouto. "Biomedical Ontologies (Semantics)"
Regular expressions originated in 1951 (Kleene 1951), so they are even older than shell scripting, but still popular and available in multiple software applications and programming languages (Forta 2018).
Page 8, added by fcouto. "Why Regular Expressions?"
how to redirect the output of a command line tool as input to another tool, or to a file. This enables the construction of pipelines of sequential invocations of command line tools.
Page 8, added by fcouto. "Pipelines"
how the semantics encoded in an ontology can be used to expand a search by adding the ancestors and related classes of a given entity
Page 8, added by fcouto. "Semantics"
Like a good physician knows that the most efficient treatment for a specific patient is not always the most advanced one, a good data scientist knows that the most efficient tool to address a specific information need is not always the most advanced one.
Page 4, added by fcouto. "Why using a long-established solution?"
The solution proposed in this book has been available for more than four decades (Ritchie 1971), and it can now be used in almost every personal computer (Haines 2017).
Page 5, added by fcouto. "Why shell scripting?"
But a shell script is still a computer algorithm, so how is it different from learning another programming language?
Page 5, added by fcouto. "shell scripting versus programming"

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History

Download catalog record: RDF / JSON
November 16, 2020 Edited by MARC Bot import existing book
July 9, 2019 Edited by fcouto Edited without comment.
June 24, 2019 Edited by fcouto Edited without comment.
June 23, 2019 Edited by fcouto Edited without comment.
June 15, 2019 Created by fcouto Added new book.