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

• Format: eBook
• Language: English
• Published: MDPI - Multidisciplinary Digital Publishing Institute 2023
• Subjects: Biology, life sciences; Research & information: general; amino acids; and graph neural network; APOBEC; basal cells; binding energy; bioinformatics; biological macromolecules; carbohydrates; cell-type composition; census tract; ciliated cells; CRISPR/Cas9; cryogenic electron microscopy; cryogenic electron tomography; data science; deep learning; DNA; DNA sequencing; drug discovery; drug repositioning; drugs of abuse; electron crystallography; electronic health records; entropy; FAIR; function annotation; functional families; genome editing; goblet cells; graph neural network; immune response to smoking; kernel classifiers; kernel method; KinBase classification; KinFams; large language models; ligand binding sites; lung cancers; machine learning; macromolecular crystallography; metagenomics; micro-electron diffraction; MSA; mutational signatures; n/a; nuclear magnetic resonance spectroscopy; nucleic acids; off-targets; Open Access; pharmacovigilance; Philip Bourne; prioritization algorithm; Protein Data Bank; protein database; protein kinases; proteins; quantum machine learning; quantum metric learning; reaction template; read classification; real-world evidence; recurrent neural network; retrosynthesis; RNA; search tool; SHAP values; small-molecule ligands; smoking; social determinants of health; social media; specificity annotation; structural bioinformatics; structure-based drug discovery; variability; Worldwide Protein Data Bank
• Online Access: Open Access: DOAB: description of the publication; Open Access: DOAB, download the publication
• Physical Description: 1 online resource (232 p.)
• ISBN: 9783036586106; 9783036586113; books978-3-0365-8610-6
• Access: Open Access

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.