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000724220 019__ $$a908088569
000724220 020__ $$a9783319109848$$qelectronic book
000724220 020__ $$a3319109847$$qelectronic book
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000724220 08204 $$a621.36/7$$223
000724220 24500 $$aSignal and image analysis for biomedical and life sciences$$h[electronic resource] /$$cChangming Sun, Tomasz Bednarz, Tuan D. Pham, Pascal Vallotton, Dadong Wang, editors.
000724220 264_1 $$aCham :$$bSpringer,$$c[2014]
000724220 264_4 $$c©2015
000724220 300__ $$a1 online resource (xvi, 276 pages) :$$billustrations (some color).
000724220 336__ $$atext$$btxt$$2rdacontent
000724220 337__ $$acomputer$$bc$$2rdamedia
000724220 338__ $$aonline resource$$bcr$$2rdacarrier
000724220 4901_ $$aAdvances in Experimental Medicine and Biology,$$x0065-2598 ;$$vvolume 823
000724220 500__ $$aIncludes index.
000724220 5050_ $$aPreface; Contents; Contributors; Acronyms; Part I Signal Analysis; 1 Visual Analytics of Signalling Pathways Using Time Profiles; 1.1 Introduction; 1.1.1 Challenges in Visualising High-Throughput Time-Series Post-translationally Modified Proteomic Datasets; 1.1.2 Aims; 1.2 Methods; 1.2.1 Phosphorylation Dataset for Insulin Response; 1.2.1.1 Data Representation; 1.2.2 Heat Map of the Time-Series Data; 1.2.2.1 Selecting a Single Time Point for Each Phosphorylation; 1.2.3 The Minardo Layout; 1.3 Results; 1.3.1 Evaluation of the Minardo Visualisation Strategy; 1.3.1.1 Requested Features
000724220 5058_ $$a1.3.2 Minardo in the International DREAM8 Competition1.3.2.1 Proposed Workflow; 1.4 Discussion and Further Work; 1.4.1 Minardo as a Web-Based Tool; 1.4.2 Lessons from the Usability Study; 1.4.3 Using 3D Structure Information; 1.4.4 Going Beyond Static Roadmaps; 1.4.5 Visualisation for Multiple Experiments; 1.4.6 Limitations; References; 2 Modeling of Testosterone Regulation by Pulse-ModulatedFeedback; 2.1 Introduction; 2.2 A Pulse-Modulated Mathematical Model of Testosterone Regulation; 2.3 Parameter Estimation; 2.3.1 Estimating the GnRH Impulses; 2.3.1.1 Estimating Firing Times and Weights
000724220 5058_ $$a2.3.1.2 Estimating the Parameters2.3.2 Estimating the Testosterone Dynamics; 2.4 Experimental Results; 2.5 Simulations of the Closed-Loop System; References; 3 Hybrid Algorithms for Multiple Change-Point Detection in Biological Sequences; 3.1 Introduction; 3.2 Multiple Change-Point Problem; 3.3 Framework of the Algorithms; 3.3.1 Quickest Change-Point Detection; 3.3.2 The Cross-Entropy Method; 3.3.2.1 Bonferroni Correction for Multiple Hypothesis Testing; 3.4 Numerical Results; 3.4.1 Results on Artificially Generated Data; 3.4.2 Results on Real Data; 3.4.2.1 Fibroblast Cell Lines Data
000724220 5058_ $$a3.4.2.2 Breast Tumor DataReferences; 4 Stochastic Anomaly Detection in Eye-Tracking Data for Quantification of Motor Symptoms in Parkinson's Disease; 4.1 Introduction; 4.2 The Extraocular Muscles; 4.3 Smooth Pursuit; 4.4 Eye Tracking; 4.5 Parkinson's Disease; 4.6 Probability Density Estimation; 4.6.1 Stochastic Variables; 4.6.2 Kernel Density Estimation; 4.6.3 Orthogonal Series Approximation; 4.6.4 Finding the Outlier Region; 4.7 Non-parametric Method; 4.8 Parametric Method; 4.9 Visual Stimuli; 4.10 Experiment; 4.11 Results; 4.11.1 Non-parametric Method; 4.11.2 Parametric Method; References
000724220 5058_ $$a5 Identification of the Reichardt Elementary Motion Detector Model5.1 Background; 5.2 Mathematical Model of EMD; 5.2.1 Single Frequency Sinusoidal Signal; 5.2.1.1 Symmetrical and Non-symmetrical EMD Model; 5.2.2 EMD Response to a L2 Pulse; 5.3 Identification Approach; 5.3.1 Identification of a Single EMD; 5.3.1.1 Pure Time-Delay Model; 5.3.2 Identification of a Layer of EMDs; 5.3.2.1 Identifiability Properties for EMD-Layer Estimation; 5.3.2.2 Spatial Excitation of a Sum of Sinusoidal Gratings: An Example; 5.3.2.3 Visualization; 5.4 Experiments; 5.4.1 Periodicity in the Experimental Data
000724220 506__ $$aAccess limited to authorized users.
000724220 520__ $$aWith an emphasis on applications of computational models for solving modern challenging problems in biomedical and life sciences, this book aims to bring collections of articles from biologists, medical/biomedical and health science researchers together with computational scientists to focus on problems at the frontier of biomedical and life sciences. The goals of this book are to build interactions of scientists across several disciplines and to help industrial users apply advanced computational techniques for solving practical biomedical and life science problems. This book is for users in t.
000724220 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 15, 2014).
000724220 650_0 $$aImage analysis.
000724220 650_0 $$aImaging systems in biology.
000724220 650_0 $$aImaging systems in medicine.
000724220 650_0 $$aSignal processing$$xDigital techniques.
000724220 7001_ $$aSun, Changming,$$eeditor.
000724220 77608 $$iPrint version:$$aSun, Changming$$tSignal and Image Analysis for Biomedical and Life Sciences$$dCham : Springer International Publishing,c2014$$z9783319109831
000724220 830_0 $$aAdvances in experimental medicine and biology ;$$vvolume 823.
000724220 852__ $$bebk
000724220 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-10984-8$$zOnline Access$$91397441.1
000724220 909CO $$ooai:library.usi.edu:724220$$pGLOBAL_SET
000724220 980__ $$aEBOOK
000724220 980__ $$aBIB
000724220 982__ $$aEbook
000724220 983__ $$aOnline
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