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Martin Aslett,
Dr. Anna Protasio,
Matthew Dorman,
Dr. Christine Boinett,
and
Dr. Ulrike Böhme
Topics Covered
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Reading list
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Provides a broad overview of bioinformatics, covering topics such as molecular biology, computer science, and statistics. It good resource for students who are interested in learning more about the field.
Provides a detailed overview of bioinformatics algorithms. It covers topics such as sequence alignment, gene finding, and protein structure prediction. It good resource for students and researchers who are interested in developing new bioinformatics algorithms.
Provides a detailed overview of statistical methods used in bioinformatics. It covers topics such as data analysis, machine learning, and statistical modeling. It good resource for students and researchers who are interested in using statistical methods to analyze biological data.
Provides a detailed overview of machine learning methods used in bioinformatics. It covers topics such as supervised learning, unsupervised learning, and feature selection. It good resource for students and researchers who are interested in using machine learning methods to analyze biological data.
Provides a detailed overview of deep learning methods used in bioinformatics. It covers topics such as convolutional neural networks, recurrent neural networks, and autoencoders. It good resource for students and researchers who are interested in using deep learning methods to analyze biological data.
Provides a detailed overview of big data in bioinformatics. It covers topics such as data management, data analysis, and data visualization. It good resource for students and researchers who are interested in working with big data in bioinformatics.
Provides a detailed overview of programming for bioinformatics using Python. It covers topics such as data structures, algorithms, and machine learning. It good resource for students and researchers who are interested in developing bioinformatics software.
Provides a detailed overview of data analysis for bioinformatics using R. It covers topics such as data wrangling, data visualization, and statistical modeling. It good resource for students and researchers who are interested in using R to analyze biological data.
Provides a detailed guide to DNA sequencing protocols, including sample preparation, library preparation, and sequencing analysis. It valuable resource for researchers who are new to DNA sequencing or who want to improve their sequencing skills.
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Provides a practical guide to DNA sequencing, including choosing the right sequencing technology, preparing samples, and analyzing data. It valuable resource for researchers who are new to DNA sequencing or who want to improve their sequencing skills.
Provides a laboratory manual for DNA sequencing. It valuable resource for researchers who are new to DNA sequencing or who want to improve their sequencing skills.
Provides a comprehensive overview of protein structure and function, covering topics such as protein folding, protein-ligand interactions, and protein dynamics. It is written in a clear and concise style, and it is suitable for both undergraduate and graduate students.
Provides a comprehensive overview of biochemistry, including a chapter on protein structure and function. It is written in a clear and concise style, and it is suitable for both undergraduate and graduate students.
Provides a comprehensive overview of molecular biology, including a chapter on protein structure and function. It is written in a clear and concise style, and it is suitable for both undergraduate and graduate students.
Provides a comprehensive overview of biochemistry, including a chapter on protein structure and function. It is written in a clear and concise style, and it is suitable for both undergraduate and graduate students.
Provides a comprehensive overview of protein bioinformatics, including chapters on protein structure prediction, protein-protein interactions, and protein function prediction. It is written in a clear and concise style, and it is suitable for both undergraduate and graduate students.
Provides a comprehensive overview of protein science, including chapters on protein structure, protein function, and protein evolution. It is written in a clear and concise style, and it is suitable for both undergraduate and graduate students.
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Provides a comprehensive overview of protein-ligand interactions, including chapters on the different types of protein-ligand interactions, the methods used to study protein-ligand interactions, and the applications of protein-ligand interactions in drug discovery. It is written in a clear and concise style, and it is suitable for both undergraduate and graduate students.
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Provides a comprehensive overview of the protein folding problem, including chapters on the different theories of protein folding, the experimental methods used to study protein folding, and the computational methods used to predict protein folding. It is written in a clear and concise style, and it is suitable for both undergraduate and graduate students.
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Provides a comprehensive overview of bioinformatics in German. It covers topics such as DNA sequencing, gene expression analysis, and protein structure prediction. It good resource for students and researchers who are interested in learning more about the field in German.
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