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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.
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    Chapter 19 Modeling and Simulation Tools: From Systems Biology to Systems Medicine.
Overall attention for this book and its chapters
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

20 tweeters
2 Facebook pages


9 Dimensions

Readers on

78 Mendeley
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Systems Medicine
Published by
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3283-2
Pubmed ID
978-1-4939-3282-5, 978-1-4939-3283-2

Caie, Peter D, Harrison, David J, Bowness, Ruth, Schmitz, Ulf, Wolkenhauer, Olaf


Ulf Schmitz, Olaf Wolkenhauer


The field of pathology is rapidly transforming from a semiquantitative and empirical science toward a big data discipline. Large data sets from across multiple omics fields may now be extracted from a patient's tissue sample. Tissue is, however, complex, heterogeneous, and prone to artifact. A reductionist view of tissue and disease progression, which does not take this complexity into account, may lead to single biomarkers failing in clinical trials. The integration of standardized multi-omics big data and the retention of valuable information on spatial heterogeneity are imperative to model complex disease mechanisms. Mathematical modeling through systems pathology approaches is the ideal medium to distill the significant information from these large, multi-parametric, and hierarchical data sets. Systems pathology may also predict the dynamical response of disease progression or response to therapy regimens from a static tissue sample. Next-generation pathology will incorporate big data with systems medicine in order to personalize clinical practice for both prognostic and predictive patient care.

Twitter Demographics

The data shown below were collected from the profiles of 20 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
Unknown 77 99%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 4%
Researcher 3 4%
Student > Ph. D. Student 2 3%
Other 2 3%
Student > Master 2 3%
Other 4 5%
Unknown 62 79%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 5%
Biochemistry, Genetics and Molecular Biology 3 4%
Pharmacology, Toxicology and Pharmaceutical Science 2 3%
Medicine and Dentistry 2 3%
Computer Science 2 3%
Other 3 4%
Unknown 62 79%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 07 February 2018.
All research outputs
of 14,155,545 outputs
Outputs from Methods in molecular biology
of 8,739 outputs
Outputs of similar age
of 339,695 outputs
Outputs of similar age from Methods in molecular biology
of 1,136 outputs
Altmetric has tracked 14,155,545 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,739 research outputs from this source. They receive a mean Attention Score of 2.1. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 339,695 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 1,136 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.