Titlebook: Mathematica for Bioinformatics; A Wolfram Language A George Mias Book 2018 Springer International Publishing AG, part of Springer Nature 20

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Metabolomics Example,all molecules in cells is termed the metabolome and its study metabolomics. In this chapter we will show an example of analysis of metabolomics data from small molecules intensity data using the Wolfram Language. The approach will involve normalizing the data, carrying out differential expression an

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Graphs and Networks,d problems involving networks, particularly in the context of biochemical reactions, and more recently regulatory networks and gene/protein interaction networks are being used to analyze large datasets (see for example Junker and Schreiber, Analysis of biological networks, Wiley, New York, 2011, [.]

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Time Series Analysis,ssentially static. This includes the study of development, disease, and the action of drugs and aging. Continuous dynamic interactions at different spatial and temporal scales are ubiquitous and only through modeling the dynamics can we achieve a systems level knowledge in biology and genetics (Bar-

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Epilog: Bioinformatics Development with Mathematica,we have used are specific to the task at hand, and in some ways customized to aid us. We have occasionally written functions for repetitive tasks, and used packages such as MathIOmica where code has been already written to help our analyses. As you develop your own code base in the Wolfram Language,

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A Wolfram Language Primer for Bioinformaticians,Mathematica [.] was released in 1988 by Wolfram Research (founded in 1987 by Steven Wolfram). The core of Mathematica is built on the Wolfram Language [.] which can be used to code programs. The Wolfram Language has broad applicability in multiple areas.

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Genomic Sequence Data and BLAST,In this chapter we consider Sequence Data and alignment using the Basic Local Alignment Search Tool (BLAST) to be able to compare nucleotide sequence information, accessing the web version through the Wolfram Language.