Assessing the Heterogeneity of Cardiac Non-myocytes and the Effect of Cell Culture with Integrative Single Cell Analysis

Bs, Iskra; Davis, Logan R.; He, Miller; Chiu, Yi-Chang; Ar, Bishop; Chen, Y; Aune, Gregory J.

doi:10.1101/2020.03.04.975177

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…To illustrate the local publication landscape, the app provides a visualization of the article ‘s position within our training data. We used SAUCIE [60], an autoencoder designed to cluster single cell RNA-seq data, to build a two-dimensional embedding space that could be applied to newly generated preprints without retraining, a limitation of other approaches that we explored for visualizing entities expected to lie on a nonlinear manifold. We trained this model on document embeddings of PMC articles that did not contain a matching preprint version.…”

Section: Methodsmentioning

confidence: 99%

Linguistic Analysis of the bioRxiv Preprint Landscape

Nicholson¹,

Rubinetti²,

Hu³

et al. 2021

Preprint

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Preprints allow researchers to make their findings available to the scientific community before they have undergone peer review. Studies on preprints within bioRxiv have been largely focused on article metadata and how often these preprints are downloaded, cited, published, and discussed online. A missing element that has yet to be examined is the language contained within the bioRxiv preprint repository. We sought to compare and contrast linguistic features within bioRxiv preprints to published biomedical text as a whole as this is an excellent opportunity to examine how peer review changes these documents. The most prevalent features that changed appear to be associated with typesetting and mentions of supplementary sections or additional files. In addition to text comparison, we created document embeddings derived from a preprint-trained word2vec model. We found that these embeddings are able to parse out different scientific approaches and concepts, link unannotated preprint-peer reviewed article pairs, and identify journals that publish linguistically similar papers to a given preprint. We also used these embeddings to examine factors associated with the time elapsed between the posting of a first preprint and the appearance of a peer reviewed publication. We found that preprints with more versions posted and more textual changes took longer to publish. Lastly, we constructed a web application (https://greenelab.github.io/preprint-similarity-search/) that allows users to identify which journals and articles that are most linguistically similar to a bioRxiv or medRxiv preprint as well as observe where the preprint would be positioned within a published article landscape.

show abstract

Section: Methodsmentioning

confidence: 99%

Linguistic Analysis of the bioRxiv Preprint Landscape

Nicholson¹,

Rubinetti²,

Hu³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The app provides a visualization of the article’s position within our training data to illustrate the local publication landscape. We used SAUCIE [ 63 ], an autoencoder designed to cluster single-cell RNA-seq data, to build a two-dimensional embedding space that could be applied to newly generated preprints without retraining, a limitation of other approaches that we explored for visualizing entities expected to lie on a nonlinear manifold. We trained this model on document embeddings of PMC articles that did not contain a matching preprint version.…”

Section: Methodsmentioning

confidence: 99%

Examining linguistic shifts between preprints and publications

Nicholson

Rubinetti

et al. 2022

PLoS Biol

View full text Add to dashboard Cite

Preprints allow researchers to make their findings available to the scientific community before they have undergone peer review. Studies on preprints within bioRxiv have been largely focused on article metadata and how often these preprints are downloaded, cited, published, and discussed online. A missing element that has yet to be examined is the language contained within the bioRxiv preprint repository. We sought to compare and contrast linguistic features within bioRxiv preprints to published biomedical text as a whole as this is an excellent opportunity to examine how peer review changes these documents. The most prevalent features that changed appear to be associated with typesetting and mentions of supporting information sections or additional files. In addition to text comparison, we created document embeddings derived from a preprint-trained word2vec model. We found that these embeddings are able to parse out different scientific approaches and concepts, link unannotated preprint–peer-reviewed article pairs, and identify journals that publish linguistically similar papers to a given preprint. We also used these embeddings to examine factors associated with the time elapsed between the posting of a first preprint and the appearance of a peer-reviewed publication. We found that preprints with more versions posted and more textual changes took longer to publish. Lastly, we constructed a web application (https://greenelab.github.io/preprint-similarity-search/) that allows users to identify which journals and articles that are most linguistically similar to a bioRxiv or medRxiv preprint as well as observe where the preprint would be positioned within a published article landscape.

show abstract

Assessing the Heterogeneity of Cardiac Non-myocytes and the Effect of Cell Culture with Integrative Single Cell Analysis

Cited by 2 publications

References 18 publications

Linguistic Analysis of the bioRxiv Preprint Landscape

Linguistic Analysis of the bioRxiv Preprint Landscape

Examining linguistic shifts between preprints and publications

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