Computational Medicine [electronic resource] : Tools and Challenges /
Trajanoski, Zlatko.editor.
Computational methodologies and modeling play a growing role for investigating mechanisms, and for the diagnosis and therapy of human diseases. This progress gave rise to computational medicine, an interdisciplinary field at the interface of computer science and medicine. The main focus of computational medicine lies in the development of data analysis methods and mathematical modeling as well as computational simulation techniques specifically addressing medical problems. In this book, we present a number of computational medicine topics at several scales: from molecules to cells, organs, and organisms. At the molecular level, tools for the analysis of genome variations as well as cloud computing resources for medical genetics are reviewed. Then, an analysis of gene expression data and the application to the characterization of microbial communities are highlighted. At the protein level, two types of analyses for mass spectrometry data are reviewed: labeled quantitative proteomics and lipidomics, followed by protein sequence analysis and a 3D structure and drug design chapter. Finally, three chapters on clinical applications focus on the integration of biomolecular and clinical data for cancer research, biomarker discovery, and network-based methods for computational diagnostics.
Description based upon print version of record.
Includes bibliographical references and index.
1. Bioinformatics tools for the search of disease-associated variations. Stefan Coassin, Anita Kloss-Brandstätter, Florian Kronenberg -- 2. Cloud computing: bringing computational power to medical genetics. Lukas Forer, Sebastian Schönherr, Hansi Weißensteiner, Günther Specht, Florian Kronenberg, Anita Kloss-Brandstätter -- 3. High-throughput characterization and comparison of microbial communities. Bettina Halwachs, Gregor Gorkiewicz, Gerhard G. Thallinger -- 4. Microarray Meta-Analysis: From Data to Expression to Biological Relationships. Julia Feichtinger, Gerhard G. Thallinger, Ramsay J. McFarlane, Lee Larcombe -- 5. Analysis of labeled quantitative mass spectrometry proteomics data. Florian Breitwieser, Jacques Colinge -- 6. Lipidomics, Mass Spectrometry and Bioinformatics. Jürgen Hartler, Harald C. Köfeler, Christopher J.O. Baker, Ravi Tharakan, Gerhard G. Thallinger -- 7. Protein sequence-structure-function-network links discoveries with the ANNOTATOR software suite: Application to ELYS/Mel-28analysis. Georg Schneider, Westley Sherman, Durga Kuchibhatla, Ooi Hong Sain, Fernanda L. Sirota, Sebastian Maurer-Stroh, Birgit Eisenhaber, Frank Eisenhaber -- 8. 3D structure and drug design. Kristina Djinovic-Carugo, Oliviero Carugo -- 9. Integrating biomolecular and clinical data for cancer research: concepts and challenges. Pornpimol Charoentong, Hubert Hackl, Bernhard Mlecnik, Gabriela Bindea, Jerome Galon, Zlatko Trajanoski -- 10. Applied data mining: from biomarker discovery to decision support systems. Melanie Osl, Michael Netzer, S. Dreiseitl, Christian Baumgartner -- 11. Network-based methods for computational diagnostics by means of R. Laurin A.J. Müller, Mathias Dehmer, Frank Emmert-Streib.
Computational methodologies and modeling play a growing role for investigating mechanisms, and for the diagnosis and therapy of human diseases. This progress gave rise to computational medicine, an interdisciplinary field at the interface of computer science and medicine. The main focus of computational medicine lies in the development of data analysis methods and mathematical modeling as well as computational simulation techniques specifically addressing medical problems. In this book, we present a number of computational medicine topics at several scales: from molecules to cells, organs, and organisms. At the molecular level, tools for the analysis of genome variations as well as cloud computing resources for medical genetics are reviewed. Then, an analysis of gene expression data and the application to the characterization of microbial communities are highlighted. At the protein level, two types of analyses for mass spectrometry data are reviewed: labeled quantitative proteomics and lipidomics, followed by protein sequence analysis and a 3D structure and drug design chapter. Finally, three chapters on clinical applications focus on the integration of biomolecular and clinical data for cancer research, biomarker discovery, and network-based methods for computational diagnostics.
Molecular biology.
Bioinformatics.
Cell biology.
Molecular Medicine.
Bioinformatics.
Cell Biology.