Evidence map›Paper›PMID 28686463›Full record

ArticleJournal of computational biology : a journal of computational molecular cell biology2017

Evolution and Vaccination of Influenza Virus.

Ham Ching Lam, Xuan Bi, Srinand Sreevatsan, Daniel Boley

Abstract read
In one paragraph

Article in Journal of computational biology : a journal of computational molecular cell biology, 2017. 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

4 authors.

Ham Ching Lam1 Minnesota Supercomputing Institute, University of Minnesota-Twin Cities Campus , Minneapolis, Minnesota.
Xuan Bi2 Department of Computer Science and Engineering, University of Minnesota-Twin Cities Campus , Minneapolis, Minnesota.
Srinand Sreevatsan3 Department of Veterinary Population Medicine, University of Minnesota-Twin Cities Campus , St. Paul, Minnesota.
Daniel Boley2 Department of Computer Science and Engineering, University of Minnesota-Twin Cities Campus , Minneapolis, Minnesota.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, we present an application paradigm in which an unsupervised machine learning approach is applied to the high-dimensional influenza genetic sequences to investigate whether vaccine is a driving force to the evolution of influenza virus. We first used a visualization approach to visualize the evolutionary paths of vaccine-controlled and non-vaccine-controlled influenza viruses in a low-dimensional space. We then quantified the evolutionary differences between their evolutionary trajectories through the use of within- and between-scatter matrices computation to provide the statistical confidence to support the visualization results. We used the influenza surface Hemagglutinin (HA) gene for this study as the HA gene is the major target of the immune system. The visualization is achieved without using any clustering methods or prior information about the influenza sequences. Our results clearly showed that the evolutionary trajectories between vaccine-controlled and non-vaccine-controlled influenza viruses are different and vaccine as an evolution driving force cannot be completely eliminated.

Indexed as

Evolution, MolecularHemagglutinin Glycoproteins, Influenza VirusHumansInfluenza A virusInfluenza, HumanInfluenza VaccinesHemagglutinin Glycoproteins, Influenza VirusInfluenza Vaccinesinfluenzamachine learningvisualization.

Identifiers

PMID28686463
PMCPMC5563926

What OpenQuestion holds

Textmetadata
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.