Evidence map›Paper›PMID 30533503›Full record

ArticleApplied network science2016

Supporting novel biomedical research via multilayer collaboration networks.

Konstantin Kuzmin, Xiaoyan Lu, Partha Sarathi Mukherjee, Juntao Zhuang, Chris Gaiteri, Boleslaw K Szymanski

Abstract read
In one paragraph

Article in Applied network science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Konstantin Kuzmin1Network Science and Technology Center, Rensselaer Polytechnic Institute (RPI), 110 Eighth Street, Troy, NY, 12180 USA.
Xiaoyan Lu *1Network Science and Technology Center, Rensselaer Polytechnic Institute (RPI), 110 Eighth Street, Troy, NY, 12180 USA.
Partha Sarathi Mukherjee *1Network Science and Technology Center, Rensselaer Polytechnic Institute (RPI), 110 Eighth Street, Troy, NY, 12180 USA.
Juntao Zhuang *1Network Science and Technology Center, Rensselaer Polytechnic Institute (RPI), 110 Eighth Street, Troy, NY, 12180 USA.
Chris Gaiteri2Rush University Medical Center, Rush University, 1653 W. Congress Parkway, Chicago, IL, 60612 USA.
Boleslaw K Szymanski1Network Science and Technology Center, Rensselaer Polytechnic Institute (RPI), 110 Eighth Street, Troy, NY, 12180 USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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 tool that helps to resolve the tension between safe/fundable research vs. high-risk/potentially transformational research. It does this by identifying hidden overlapping interests around novel molecular research topics. Specifically, it identifies paths of molecular interactions that connect research topics and hypotheses that would not typically be associated, as the basis for scientific collaboration. Because these collaborations are related to the scientists' present trajectory, they are low risk and can be initiated rapidly. Unlike most incremental steps, these collaborations have the potential for leaps in understanding, as they reposition research for novel disease applications. We demonstrate the use of this tool to identify scientists who could contribute to understanding the cellular role of genes with novel associations with Alzheimer's disease, which have not been thoroughly characterized, in part due to the funding emphasis on established research.

Indexed as

Coauthorship networksCollaboration networksDisruptive scienceHeterogeneous multilayer networksMolecular networksNeo4jNetwork analysisPersonalized PageRankPubMed

Identifiers

PMID30533503
PMCPMC6245218

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.