The Resource Signal and image analysis for biomedical and life sciences, Changming Sun, Tomasz Bednarz, Tuan D. Pham, Pascal Vallotton, Dadong Wang, editors

Signal and image analysis for biomedical and life sciences, Changming Sun, Tomasz Bednarz, Tuan D. Pham, Pascal Vallotton, Dadong Wang, editors

Label
Signal and image analysis for biomedical and life sciences
Title
Signal and image analysis for biomedical and life sciences
Statement of responsibility
Changming Sun, Tomasz Bednarz, Tuan D. Pham, Pascal Vallotton, Dadong Wang, editors
Contributor
Editor
Subject
Genre
Language
eng
Summary
With 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
Member of
Cataloging source
N$T
Dewey number
621.36/7
Illustrations
illustrations
Index
index present
LC call number
TA1637
LC item number
.S54 2015eb
Literary form
non fiction
Nature of contents
dictionaries
NLM call number
QT 36.2
http://library.link/vocab/relatedWorkOrContributorName
Sun, Changming
Series statement
Advances in Experimental Medicine and Biology,
Series volume
volume 823
http://library.link/vocab/subjectName
  • Image analysis
  • Imaging systems in biology
  • Imaging systems in medicine
  • Signal processing
  • TECHNOLOGY & ENGINEERING
  • Image analysis
  • Imaging systems in biology
  • Imaging systems in medicine
  • Signal processing
  • Computer Science
  • Image Processing and Computer Vision
  • Biomedicine general
  • Life Sciences, general
  • Signal Processing, Computer-Assisted
  • Medical research
  • Life sciences: general issues
  • Image processing
Label
Signal and image analysis for biomedical and life sciences, Changming Sun, Tomasz Bednarz, Tuan D. Pham, Pascal Vallotton, Dadong Wang, editors
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-319-10984-8
Instantiates
Publication
Copyright
Note
Includes index
Antecedent source
unknown
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Preface; 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
  • 1.3.2 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
  • 2.3.1.2 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
  • 3.4.2.2 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
  • 5 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
Dimensions
unknown
Extent
1 online resource (xvi, 276 pages)
File format
unknown
Form of item
online
Isbn
9783319109831
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Note
SpringerLink
Other control number
10.1007/978-3-319-10984-8
Other physical details
illustrations (some color).
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • (OCoLC)894893465
  • (OCoLC)ocn894893465
Label
Signal and image analysis for biomedical and life sciences, Changming Sun, Tomasz Bednarz, Tuan D. Pham, Pascal Vallotton, Dadong Wang, editors
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-319-10984-8
Publication
Copyright
Note
Includes index
Antecedent source
unknown
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Preface; 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
  • 1.3.2 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
  • 2.3.1.2 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
  • 3.4.2.2 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
  • 5 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
Dimensions
unknown
Extent
1 online resource (xvi, 276 pages)
File format
unknown
Form of item
online
Isbn
9783319109831
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Note
SpringerLink
Other control number
10.1007/978-3-319-10984-8
Other physical details
illustrations (some color).
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • (OCoLC)894893465
  • (OCoLC)ocn894893465

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