Supporting novel biomedical research via multilayer collaboration networks

@article{Kuzmin2016SupportingNB,
  title={Supporting novel biomedical research via multilayer collaboration networks},
  author={Konstantin Kuzmin and Xiaoyan Lu and Partha Sarathi Mukherjee and Juntao Zhuang and Chris Gaiteri and Boleslaw K. Szymanski},
  journal={Applied Network Science},
  year={2016},
  volume={1}
}
The value of research containing novel combinations of molecules can be seen in many innovative and award-winning research programs. Despite calls to use innovative approaches to address common diseases, an increasing majority of research funding goes toward “safe” incremental research. Counteracting this trend by nurturing novel and potentially transformative scientific research is challenging and it must be supported in competition with established research programs. Therefore, we propose a… 
A Recommender for Research Collaborators Using Graph Neural Networks
TLDR
A collaboration recommendation system based on novel graph neural networks, i.e., GraphSAGE and Temporal Graph Network, which can capture intrinsic, complex, and changing dependencies among researchers, including temporal user–user interactions is developed.
Network analysis to support public health: evolution of collaboration among leishmaniasis researchers
TLDR
The results show that network analysis metrics can bring evidences of evolution of collaboration between different research groups within a specific research area and that those areas have subnetworks that influence collaboration structures and focus.
Link prediction for interdisciplinary collaboration via co-authorship network
TLDR
This work analyzes the Publication and Research data set of University of Bristol and proposes a new link prediction methodology, with the specific aim of identifying potential interdisciplinary collaboration in a university-wide collaboration network.
SEARCH ALGORITHMS FOR PROMOTION OF NOVEL BIOMEDICAL RESEARCH By
TLDR
This document summarizes current capabilities, research and operational priorities, and plans for further studies that were established at the 2015 USGS workshop on quantitative hazard assessments of earthquake-triggered landsliding and liquefaction in the Czech Republic.

References

SHOWING 1-10 OF 55 REFERENCES
Synergy Landscapes: A Multilayer Network for Collaboration in Biological Research
TLDR
This work combines the structure of molecular interaction networks with other science networks, such as coauthorship networks, for a more complete representation of the interests and relationships that determine the direction and impact of research.
Choosing experiments to accelerate collective discovery
TLDR
A generative model of discovery informed by qualitative research on scientific problem selection is built and it is found that increased risk-taking and the publication of experimental failures would substantially improve the speed of discovery.
Tradition and Innovation in Scientists’ Research Strategies
TLDR
By studying prizewinners in biomedicine and chemistry, it is shown that occasional gambles for extraordinary impact are a compelling explanation for observed levels of risky innovation.
The National Institutes of Health's Big Data to Knowledge (BD2K) initiative: capitalizing on biomedical big data
TLDR
The BD2K initiative seeks to better define how to extract value from the data, both for the individual investigator and the overall research community, and create the analytic tools needed to enhance utility of the data.
Genetic variants in Alzheimer disease — molecular and brain network approaches
TLDR
How the study of molecular, cellular and brain networks provides additional information on the effects of LOAD-associated genetic variants at multiple biophysical scales is discussed and the clinical potential of mechanistically coupling genetic variants and disease phenotypes with multiscale brain models is highlighted.
Accelerating scientific publication in biology
  • Ronald D Vale
  • Education
    Proceedings of the National Academy of Sciences
  • 2015
TLDR
The analysis suggests that publication practices have changed considerably in the life sciences over the past 30 years, and the average time required for graduate students to publish their first paper has increased and is approaching the desirable duration of PhD training.
Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality.
  • M. Newman
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2001
TLDR
It is argued that simple networks such as these cannot capture variation in the strength of collaborative ties and proposed a measure of collaboration strength based on the number of papers coauthored by pairs of scientists, and thenumber of other scientists with whom they coauthored those papers.
The structure of scientific collaboration networks.
  • M. Newman
  • Physics
    Proceedings of the National Academy of Sciences of the United States of America
  • 2001
TLDR
It is shown that these collaboration networks form "small worlds," in which randomly chosen pairs of scientists are typically separated by only a short path of intermediate acquaintances.
...
...