Bioinformatics with Python Cookbook
Use modern Python libraries and applications to solve real-world computational biology problems
3. Auflage
Erschienen am 23. September 2024
360 Seiten
978-1-80324-772-4 (ISBN)
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No detailed description available for "Bioinformatics with Python Cookbook".
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Antao Tiago :
Tiago Antao is a bioinformatician currently working in the field of genomics. A former computer scientist, Tiago moved into computational biology with an MSc in Bioinformatics from the Faculty of Sciences at the University of Porto (Portugal) and a PhD on the spread of drug-resistant malaria from the Liverpool School of Tropical Medicine (UK). Postdoctoral, Tiago has worked with human datasets at the University of Cambridge (UK) and with mosquito whole genome sequencing data at the University of Oxford (UK), before helping to set up the bioinformatics infrastructure at the University of Montana. He currently works as a data engineer in the biotechnology field in Boston, MA. He is one of the co-authors of Biopython, a major bioinformatics package written in Python.
Tiago Antao is a bioinformatician currently working in the field of genomics. A former computer scientist, Tiago moved into computational biology with an MSc in Bioinformatics from the Faculty of Sciences at the University of Porto (Portugal) and a PhD on the spread of drug-resistant malaria from the Liverpool School of Tropical Medicine (UK). Postdoctoral, Tiago has worked with human datasets at the University of Cambridge (UK) and with mosquito whole genome sequencing data at the University of Oxford (UK), before helping to set up the bioinformatics infrastructure at the University of Montana. He currently works as a data engineer in the biotechnology field in Boston, MA. He is one of the co-authors of Biopython, a major bioinformatics package written in Python.
Inhalt
Table of Contents - Python and the Surrounding Software Ecology
- Using Data Processing Libraries: numpy, pandas, arrow, and zarr
- Next Generation Sequencing
- Advanced NGS Data Processing
- Working with Genomes
- Population Genetics
- Phylogenetics
- Using the Protein Data Bank
- Bioinformatics Pipelines
- Machine Learning for Bioinformatics
- Parallel Processing with Dask
- Functional and Asynchronous Programming
- Using Data Processing Libraries: numpy, pandas, arrow, and zarr
- Next Generation Sequencing
- Advanced NGS Data Processing
- Working with Genomes
- Population Genetics
- Phylogenetics
- Using the Protein Data Bank
- Bioinformatics Pipelines
- Machine Learning for Bioinformatics
- Parallel Processing with Dask
- Functional and Asynchronous Programming
