The book introduces to the reader a number of cutting edge statistical methods which can e used for the analysis of genomic, proteomic and metabolomic data sets. In particular in the field of systems biology, researchers are trying to analyze as many data as possible in a given biological system (such as a cell or an organ). The appropriate statistical evaluation of these large scale data is critical for the correct interpretation and different experimental approaches require different approaches for the statistical analysis of these data. This book is written by biostatisticians and mathematicians but aimed as a valuable guide for the experimental researcher as well computational biologists who often lack an appropriate background in statistical analysis.
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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 principles of metabolic networks.
Prior to joining Novartis Oncology in 2010, Armin Graber served as the CEO and Chancellor of the University for Health Sciences, Medical Informatics, and Technology (UMIT), in Hall, Austria, where he was also professor in the Department of Biomedical Sciences and Engineering. He has held various senior positions in biotechnology in the USA and Europe, including VP for Translational Research at BG Medicine, CEO of Biocrates life sciences AG, and Head of Bioinformatics in the Applied Biosystems Discovery Proteomics and Small Molecule Research Center. His research interests comprise targeted and non-targeted functional genomic technologies, and bioinformatic and biostatistic methods for biomarker discovery, validation and delivery.
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 Cellular States by Biological Phase Transitions (Mariano Bizzarri and Alessandro Giuliani)
Protein Network Analysis for Disease Gene Identification and Prioritization (Jing Chen and Anil G. Jegga)
Pathways and Networks as Functional Descriptors for Human Disease and Drug Response Endpoints (Y. Nikolsky and B. Bessarabova and E. Kirillov and Z. Dezso and W. Nikolskaya)
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