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Open AccessArticle

Meta-Analysis of Gene Popularity: Less Than Half of Gene Citations Stem from Gene Regulatory Networks

1
Molecular Infection Medicine Sweden (MIMS), Umeå Centre for Microbial Research, Department of Molecular Biology, Umeå University, 901 87 Umeå, Sweden
2
Industrial Doctoral School, Umeå University, 901 87 Umeå, Sweden
3
National Clinical Research School in Chronic Inflammatory Diseases (NCRSCID), Karolinska Institutet, 171 77 Solna, Sweden
*
Author to whom correspondence should be addressed.
Academic Editor: Piero Fariselli
Genes 2021, 12(2), 319; https://doi.org/10.3390/genes12020319
Received: 27 January 2021 / Revised: 14 February 2021 / Accepted: 20 February 2021 / Published: 23 February 2021
(This article belongs to the Section Technologies and Resources for Genetics)
The reasons for selecting a gene for further study might vary from historical momentum to funding availability, thus leading to unequal attention distribution among all genes. However, certain biological features tend to be overlooked in evaluating a gene’s popularity. Here we present a meta-analysis of the reasons why different genes have been studied and to what extent, with a focus on the gene-specific biological features. From unbiased datasets we can define biological properties of genes that reasonably may affect their perceived importance. We make use of both linear and nonlinear computational approaches for estimating gene popularity to then compare their relative importance. We find that roughly 25% of the studies are the result of a historical positive feedback, which we may think of as social reinforcement. Of the remaining features, gene family membership is the most indicative followed by disease relevance and finally regulatory pathway association. Disease relevance has been an important driver until the 1990s, after which the focus shifted to exploring every single gene. We also present a resource that allows one to study the impact of reinforcement, which may guide our research toward genes that have not yet received proportional attention. View Full-Text
Keywords: gene; Matthew effect; biological feature; genomics; machine learning; linear model; gene regulatory networks gene; Matthew effect; biological feature; genomics; machine learning; linear model; gene regulatory networks
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MDPI and ACS Style

Mihai, I.S.; Das, D.; Maršalkaite, G.; Henriksson, J. Meta-Analysis of Gene Popularity: Less Than Half of Gene Citations Stem from Gene Regulatory Networks. Genes 2021, 12, 319. https://doi.org/10.3390/genes12020319

AMA Style

Mihai IS, Das D, Maršalkaite G, Henriksson J. Meta-Analysis of Gene Popularity: Less Than Half of Gene Citations Stem from Gene Regulatory Networks. Genes. 2021; 12(2):319. https://doi.org/10.3390/genes12020319

Chicago/Turabian Style

Mihai, Ionut S.; Das, Debojyoti; Maršalkaite, Gabija; Henriksson, Johan. 2021. "Meta-Analysis of Gene Popularity: Less Than Half of Gene Citations Stem from Gene Regulatory Networks" Genes 12, no. 2: 319. https://doi.org/10.3390/genes12020319

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