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The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies.

Biological models. --- Speech.

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The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies.

Biological models. --- Speech.

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"For many years, experiments using chimpanzees have been instrumental in advancing scientific knowledge and have led to new medicines to prevent life-threatening and debilitating diseases. However, recent advances in alternate research tools have rendered chimpanzees largely unnecessary as research subjects. The Institute of Medicine, in collaboration with the National Research Council, conducted an in-depth analysis of the scientific necessity for chimpanzees in NIH-funded biomedical and behavioral research. The committee concludes that while the chimpanzee has been a valuable animal model in the past, most current biomedical research use of chimpanzees is not necessary, though noted that it is impossible to predict whether research on emerging or new diseases may necessitate chimpanzees in the future"--Publisher description.

Chimpanzees. --- Chimpanzees as laboratory animals --- Animal Experimentation --- Pan troglodytes --- Chimpanzee --- Chimp --- Common chimpanzee --- Robust chimpanzee --- Animal models in research. --- Pan (Mammals) --- Animal experimentation --- Biological models --- Laboratory animals --- Research

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This book presents state-of-the-art methods, software and applications surrounding weighted networks. Most methods and results also apply to unweighted networks. Although aspects of weighted network analysis relate to standard data mining methods, the intuitive network language and analysis framework transcend any particular analysis method. Weighted networks give rise to data reduction methods, clustering procedures, visualization methods, data exploratory methods, and intuitive approaches for integrating disparate data sets. Weighted networks have been used to analyze a variety of high dimensional genomic data sets including gene expression-, epigenetic-, methylation-, proteomics-, and fMRI- data. Chapters explore the fascinating topological structure of weighted networks and provide geometric interpretations of network methods. Powerful systems-level analysis methods result from combining network- with data mining methods. The book not only describes the WGCNA R package but also other software packages. Weighted gene co-expression network applications, real data sets, and exercises guide the reader on how to use these methods in practice, e.g. in systems-biologic or systems-genetic applications. The material is self-contained and only requires a minimum knowledge of statistics. The book is intended for students, faculty, and data analysts in many fields including bioinformatics, computational biology, statistics, computer science, biology, genetics, applied mathematics, physics, and social science. .

Data mining. --- Genomics. --- System analysis -- Data processing. --- System analysis. --- System analysis --- Data mining --- Life sciences --- Human genetics --- Bioinformatics --- Biological models --- Biology --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Health & Biological Sciences --- Operations Research --- Biology - General --- Data processing --- Systems biology. --- Genome research --- Genomes --- Research --- Life sciences. --- Human genetics. --- Bioinformatics. --- Computational biology. --- Life Sciences. --- Systems Biology. --- Human Genetics. --- Computer Appl. in Life Sciences. --- Computational biology --- Biological systems --- Molecular biology --- Molecular genetics --- Biological models. --- Data processing. --- Models, Biological --- Genetics --- Heredity, Human --- Human biology --- Physical anthropology --- Bio-informatics --- Biological informatics --- Information science --- Systems biology --- Bioinformatics . --- Computational biology .

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A complete introduction to the application of advanced signal processing methods to biomedical engineering problemsThis edited volume, which grew out of the GNB (Gruppo Nazionale di Bioingegneria, Italy) Summer School on Biomedical Signal Processing, explains some of the most advanced methodological signal processing techniques and applies them to biomedical engineering problems. Prominent experts in the areas of biomedical signal processing, biomedical data treatment, medicine, signal processing, system biology, and applied physiology introduce novel techniques and algorithms as well as their clinical or physiological applications.Divided into seven sections, Advanced Methods of Biomedical Signal Processing covers:. The peculiarities of biomedical signal processing with respect to more traditional applications of digital signal processing and their classification. An experimental physiologist's and cardiologist's view of the cardiovascular, central and autonomic nervous systems. An important link between biomedical signal processing and physiological modeling. Time-frequency, time-scale, and wavelet analysis. Advanced methods employed in complexity measurements. Computational genomics and proteomics. Key methods for signal classification, such as neural networks, neuro-fuzzy and genetic algorithmsThe book provides a compelling overview of techniques, such as multisource and multi-scale integration of information for physiology and clinical decision; the integration of signal processing methods with a modeling approach; complexity measurement from biomedical signals; higher order analysis in biomedical signals; and classification and parameter enhancement. Various contributions reveal that biomedical signal processing must be viewed in a wider context, with key links to the modeling phase of the signal-generating mechanisms, in order to better comprehend the behavior of the biological system under investigation.Graduate and PhD students in engineering/biomedical engineering courses, physics and applied mathematics, as well as research professionals in medical and biological sciences will highly benefit from this authoritative resource.

Signal processing. --- Biomedical engineering. --- Biomedical Engineering. --- Signal Processing, Computer-Assisted. --- Microarray Analysis --- Models, Biological. --- Biological Model --- Biological Models --- Model, Biological --- Models, Biologic --- Biologic Model --- Biologic Models --- Model, Biologic --- Clinical engineering --- Medical engineering --- Bioengineering --- Biophysics --- Engineering --- Medicine --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication) --- methods. --- Biomedical engineering --- Signal processing --- Models, Biological --- methods