Applied Statistics for Network Biology / Quantitative and Network Biology (PDF)

MODELING, SIMULATION AND MEANING OF GENE NETWORKS Network Analysis to Interpret Complex Phenotypes (Hong Yu, Jialiang Huang, Wei Zhang, and Jing-Dong J. Han) Stochastic Modelling of Regulatory Networks (Tianhai Tian) Modeling eQTL in Multiple Populations (Ching-Lin Hsiao and Cathy S.J. Fann) INFERENCE OF GENE NETWORKS Transcriptional Network Inference based on Information Theory (Patrick E. Meyer and Gianluca Bontempi) Elucidation of General and Condition-dependent Gene Pathways Using Mixture Models and Bayesian Networks (Sandra Rodriguez-Zas and Younhee Ko) Multi-scale Networks Reconstruction from Gene-expression Measurements: Correlations, Perturbations and a-priori Biological Knowledge (Daniel Remondini and Gastone Castellani) Gene Regulatory Network Inference: Combining a Genetic Programming and Hendless Filtering Approach (Lijun Qian, Haixin Wang, and Xiangfang Li) Computational Reconstruction of Protein Interaction Networks (Konrad Mönks, Irmgard Mühlberger, Andreas Bernthaler, Raul Fechete, Paul Perco, Rudolf Freund, Arno Lukas, and Bernd Mayer) ANALYSIS OF GENE NETWORKS What if the Fit is Unfit? Criteria for Biological Systems Estimation Beyond Residual Errors (Eberhard O. Voit) Machine Learning Methods for Identifying Essential Genes and Proteins in Networks (Kitiporn Plaimas and Rainer König) Gene Co-expression Networks for the Analysis of DNA Microarray Data (Matthew Weirauch) Correlation Network Analysis and Knowledge Integration (Thomas N. Plasterer, Robert Stanley, and Erich Gombocz) Network Screening: A New Method to Identify Active Networks from an Ensemble of Known Networks (Shigeru Saito and Katsuhisa Horimoto) Community Detection in Biological Networks (Gautam S. Thakur) On Some Inverse Problems in Generating Probabilistic Boolean Networks (Xi Chen, Wai-Ki Ching and Nam-Kiu Tsing) Boolean analysis of gene-expression datasets (Debashis Sahoo) SYSTEMS APPROACH TO DISEASES Representing Cancer Cell Trajectories in a Phase-space Diagram: Switching

Matthias Dehmer studied mathematics at the University of Siegen, Germany and received his PhD in computer science from the Technical University of Darmstadt. He began his academic career as a research fellow at Vienna Bio Center, Austria, and at Vienna University of Technology, and is currently a professor for bioinformatics and systems biology at UMIT - The Health and Life Sciences University. His research interests are in bioinformatics, systems biology, complex networks and statistics. In particular, Professor Dehmer is also working on machine learning-based methods to design new data analysis methods for solving problems in computational and systems biology.
Frank Emmert-Streib studied physics at the University of Siegen, Germany, gaining his PhD in theoretical physics from the University of Bremen. He was a postdoctoral research associate at the Stowers Institute for Medical Research, Kansas City, USA, and a Senior Fellow at the University of Washington, Seattle, USA. Currently, he is a lecturer/assistant professor at the Queen's University Belfast, UK at the Center for Cancer Research and Cell Biology, heading the Computational Biology and Machine Learning Lab. His research interests are in the field of computational biology and biostatistics in the development and application of methods from statistics and machine learning for the analysis of high-throughput data from genomics and genetics experiments.
Armindo Salvador studied biochemistry at the University of Lisbon, Portugal, where he received his PhD in theoretical biochemistry. He was a postdoctoral fellow at the University of Michigan, USA, and at the University of Coimbra, Portugal. He currently heads the Molecular Systems Biology Group at the Center for Neuroscience and Cell Biology, University of Coimbra, Portugal. Dr. Salvador's research interests are in the fields of molecular systems biology and computational biology. In particular, he is working toward clarifying the naturally evolved design

Dieses eBook können Sie uneingeschränkt auf allen Geräten der tolino Familie lesen. Zum Lesen auf sonstigen eReadern und am PC benötigen Sie eine Adobe ID.