Biomolecular Data Science—in Honor of Professor Philip E. Bourne

doi:10.3390/books978-3-0365-8610-6

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Hardback

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Keywords

structure-based drug discovery; ligand binding sites; deep learning; graph neural network; CRISPR/Cas9; genome editing; machine learning; SHAP values; binding energy; off-targets; drug discovery; retrosynthesis; reaction template; machine learning; recurrent neural network; and graph neural network; mutational signatures; smoking; lung cancers; APOBEC; immune response to smoking; cell-type composition; goblet cells; ciliated cells; basal cells; Protein Data Bank; Open Access; Worldwide Protein Data Bank; macromolecular crystallography; cryogenic electron microscopy; cryogenic electron tomography; electron crystallography; micro-electron diffraction; nuclear magnetic resonance spectroscopy; biological macromolecules; proteins; nucleic acids; DNA; RNA; carbohydrates; small-molecule ligands; social determinants of health; electronic health records; real-world evidence; census tract; data science; protein database; search tool; prioritization algorithm; drug repositioning; quantum machine learning; quantum metric learning; kernel method; kernel classifiers; structural bioinformatics; function annotation; specificity annotation; MSA; entropy; variability; deep learning; amino acids; Philip Bourne; FAIR; bioinformatics; n/a; protein kinases; functional families; KinFams; KinBase classification; large language models; pharmacovigilance; social media; drugs of abuse; deep learning; DNA sequencing; read classification; metagenomics

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Biomolecular Data Science—in Honor of Professor Philip E. Bourne

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August 2023

232 pages

ISBN 978-3-0365-8611-3 (Hardback)
ISBN 978-3-0365-8610-6 (PDF)

https://doi.org/10.3390/books978-3-0365-8610-6

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This is a Reprint of the Special Issue Biomolecular Data Science—in Honor of Professor Philip E. Bourne that was published in

Biology & Life Sciences

Summary

This reprint includes 14 articles in Biomedical Data Science in honor of Professor Philip Bourne. Contributed by world-renowned experts, these articles cover a broad range of topics in machine learning, biophysics, bioinformatics, and systems biology.