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Systems Medicine

Overview of attention for book
Cover of 'Systems Medicine'

Table of Contents

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    Book Overview
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    Chapter 1 Systems Medicine: Sketching the Landscape.
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    Chapter 2 Taking Bioinformatics to Systems Medicine.
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    Chapter 3 Systems Medicine: The Future of Medical Genomics, Healthcare, and Wellness.
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    Chapter 4 Next-Generation Pathology
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    Chapter 5 Training in Systems Approaches for the Next Generation of Life Scientists and Medical Doctors.
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    Chapter 6 Systems Medicine in Pharmaceutical Research and Development.
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    Chapter 7 Systems Medicine and Infection
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    Chapter 8 Systems Medicine for Lung Diseases: Phenotypes and Precision Medicine in Cancer, Infection, and Allergy.
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    Chapter 9 Third-Kind Encounters in Biomedicine: Immunology Meets Mathematics and Informatics to Become Quantitative and Predictive.
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    Chapter 10 Systems Medicine in Oncology: Signaling Network Modeling and New-Generation Decision-Support Systems.
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    Chapter 11 Neurological Diseases from a Systems Medicine Point of View.
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    Chapter 12 Computational Modeling of Human Metabolism and Its Application to Systems Biomedicine.
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    Chapter 13 From Systems Understanding to Personalized Medicine: Lessons and Recommendations Based on a Multidisciplinary and Translational Analysis of COPD.
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    Chapter 14 RNA Systems Biology for Cancer: From Diagnosis to Therapy.
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    Chapter 15 Mathematical Models of Pluripotent Stem Cells: At the Dawn of Predictive Regenerative Medicine.
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    Chapter 16 Network-Assisted Disease Classification and Biomarker Discovery.
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    Chapter 17 Anatomy and Physiology of Multiscale Modeling and Simulation in Systems Medicine.
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    Chapter 18 Mathematical and Statistical Techniques for Systems Medicine: The Wnt Signaling Pathway as a Case Study.
  20. Altmetric Badge
    Chapter 19 Modeling and Simulation Tools: From Systems Biology to Systems Medicine.
Attention for Chapter 7: Systems Medicine and Infection
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Chapter title
Systems Medicine and Infection
Chapter number 7
Book title
Systems Medicine
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3283-2_7
Pubmed ID
Book ISBNs
978-1-4939-3282-5, 978-1-4939-3283-2
Authors

Bowness, Ruth, Ruth Bowness

Editors

Ulf Schmitz, Olaf Wolkenhauer

Abstract

By using a systems-based approach, mathematical and computational techniques can be used to develop models that describe the important mechanisms involved in infectious diseases. An iterative approach to model development allows new discoveries to continually improve the model and ultimately increase the accuracy of predictions.SIR models are used to describe epidemics, predicting the extent and spread of disease. Genome-wide genotyping and sequencing technologies can be used to identify the biological mechanisms behind diseases. These tools help to build strategies for disease prevention and treatment, an example being the recent outbreak of Ebola in West Africa where these techniques were deployed.HIV is a complex disease where much is still to be learned about the virus and the best effective treatment. With basic mathematical modeling techniques, significant discoveries have been made over the last 20 years. With recent technological advances, the computational resources now available, and interdisciplinary cooperation, further breakthroughs are inevitable.In TB, modeling has traditionally been empirical in nature, with clinical data providing the fuel for this top-down approach. Recently, projects have begun to use data derived from laboratory experiments and clinical trials to create mathematical models that describe the mechanisms responsible for the disease.A systems medicine approach to infection modeling helps identify important biological questions that then direct future experiments, the results of which improve the model in an iterative cycle. This means that data from several model systems can be integrated and synthesized to explore complex biological systems.

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 23%
Other 6 17%
Student > Ph. D. Student 5 14%
Librarian 3 9%
Student > Master 3 9%
Other 5 14%
Unknown 5 14%
Readers by discipline Count As %
Medicine and Dentistry 6 17%
Nursing and Health Professions 6 17%
Agricultural and Biological Sciences 4 11%
Chemistry 2 6%
Veterinary Science and Veterinary Medicine 2 6%
Other 7 20%
Unknown 8 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 February 2016.
All research outputs
#18,438,457
of 22,844,985 outputs
Outputs from Methods in molecular biology
#7,921
of 13,127 outputs
Outputs of similar age
#284,426
of 393,571 outputs
Outputs of similar age from Methods in molecular biology
#846
of 1,470 outputs
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So far Altmetric has tracked 13,127 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
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