Search

Your search keyword '"Nicholson, David N."' showing total 17 results
Start Over Author "Nicholson, David N."
17 results on '"Nicholson, David N."'

5. Hetnet connectivity search provides rapid insights into how two biomedical entities are related

Author

Himmelstein, Daniel S., primary, Zietz, Michael, additional, Rubinetti, Vincent, additional, Kloster, Kyle, additional, Heil, Benjamin J., additional, Alquaddoomi, Faisal, additional, Hu, Dongbo, additional, Nicholson, David N., additional, Hao, Yun, additional, Sullivan, Blair D., additional, Nagle, Michael W., additional, and Greene, Casey S., additional

Published
2023
Full Text
View/download PDF

9. Examining linguistic shifts between preprints and publications

Author

Nicholson, David N., Rubinetti, Vincent, Hu, Dongbo, Thielk, Marvin, Hunter, Lawrence E., and Greene, Casey S.

Subjects
Science and Technology Workforce, Computer and Information Sciences, Viral Diseases, Biomedical Research, Science Policy, Bioinformatics, Social Sciences, Research and Analysis Methods, Careers in Research, Database and Informatics Methods, Mathematical and Statistical Techniques, Medical Conditions, Terminology as Topic, Medicine and Health Sciences, Statistical Methods, Scientific Publishing, Data Management, Language, Metadata, Principal Component Analysis, Grammar, Statistics, Publications, Linguistics, Covid 19, Research Assessment, Professions, Infectious Diseases, Preprints as Topic, People and Places, Multivariate Analysis, Physical Sciences, Meta-Research Article, Scientists, Population Groupings, Mathematics
Abstract

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., Preprints allow researchers to make their findings available to the scientific community before they have undergone peer review This study analyzes the full text content of the bioRxiv preprint repository, identifying field-specific patterns and changes that occur during publication, and providing a search tool that can identify the published papers that are most similar to a given bioRxiv or medRxiv preprint.

Published
2022

10. Unmasking the Language of Science through Textual Analyses on Biomedical Preprints and Published Papers

Author

Nicholson, David N and Nicholson, David N

Abstract

Scientific communication is essential for science as it enables the field to grow. This task is often accomplished through a written form such as preprints and published papers. We can obtain a high-level understanding of science and how scientific trends adapt over time by analyzing these resources. This thesis focuses on conducting multiple analyses using biomedical preprints and published papers. In Chapter 2, we explore the language contained within preprints and examine how this language changes due to the peer-review process. We find that token differences between published papers and preprints are stylistically based, suggesting that peer-review results in modest textual changes. We also discovered that preprints are eventually published and adopted quickly within the life science community. Chapter 3 investigates how biomedical terms and tokens change their meaning and usage through time. We show that multiple machine learning models can correct for the latent variation contained within the biomedical text. Also, we provide the scientific community with a listing of over 43,000 potential change points. Tokens with notable changepoints such as “sars” and “cas9” appear within our listing, providing some validation for our approach. In Chapter 4, we use the weak supervision paradigm to examine the possibility of speeding up the labeling function generation process for multiple biomedical relationship types. We found that the language used to describe a biomedical relationship is often distinct, leading to a modest performance in terms of transferability. An exception to this trend is Compound-binds-Gene and Gene-interacts-Gene relationship types.

Published
2022

12. Hetnet connectivity search provides rapid insights into how biomedical entities are related.

Author

Himmelstein, Daniel S, Zietz, Michael, Rubinetti, Vincent, Kloster, Kyle, Heil, Benjamin J, Alquaddoomi, Faisal, Hu, Dongbo, Nicholson, David N, Hao, Yun, Sullivan, Blair D, Nagle, Michael W, and Greene, Casey S

Subjects
PYTHON programming language, SUPERVISED learning, KNOWLEDGE graphs, DRUG repositioning, BREAST cancer
Abstract

Background Hetnets, short for "heterogeneous networks," contain multiple node and relationship types and offer a way to encode biomedical knowledge. One such example, Hetionet, connects 11 types of nodes—including genes, diseases, drugs, pathways, and anatomical structures—with over 2 million edges of 24 types. Previous work has demonstrated that supervised machine learning methods applied to such networks can identify drug repurposing opportunities. However, a training set of known relationships does not exist for many types of node pairs, even when it would be useful to examine how nodes of those types are meaningfully connected. For example, users may be curious about not only how metformin is related to breast cancer but also how a given gene might be involved in insomnia. Findings We developed a new procedure, termed hetnet connectivity search , that proposes important paths between any 2 nodes without requiring a supervised gold standard. The algorithm behind connectivity search identifies types of paths that occur more frequently than would be expected by chance (based on node degree alone). Several optimizations were required to precompute significant instances of node connectivity at the scale of large knowledge graphs. Conclusion We implemented the method on Hetionet and provide an online interface at https://het.io/search. We provide an open-source implementation of these methods in our new Python package named hetmatpy. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

15. The Nephila clavipes genome highlights the diversity of spider silk genes and their complex expression

Author

Babb, Paul L, Lahens, Nicholas F, Correa-Garhwal, Sandra M, Nicholson, David N, Kim, Eun Ji, Hogenesch, John B, Kuntner, Matjaž, Higgins, Linden, Hayashi, Cheryl Y, Agnarsson, Ingi, and Voight, Benjamin F

Abstract

Spider silks are the toughest known biological materials, yet are lightweight and virtually invisible to the human immune system, and they thus have revolutionary potential for medicine and industry. Spider silks are largely composed of spidroins, a unique family of structural proteins. To investigate spidroin genes systematically, we constructed the first genome of an orb-weaving spider: the golden orb-weaver (Nephila clavipes), which builds large webs using an extensive repertoire of silks with diverse physical properties. We cataloged 28 Nephila spidroins, representing all known orb-weaver spidroin types, and identified 394 repeated coding motif variants and higher-order repetitive cassette structures unique to specific spidroins. Characterization of spidroin expression in distinct silk gland types indicates that glands can express multiple spidroin types. We find evidence of an alternatively spliced spidroin, a spidroin expressed only in venom glands, evolutionary mechanisms for spidroin diversification, and non-spidroin genes with expression patterns that suggest roles in silk production.

Published
2017
Full Text
View/download PDF

17. Hetnet connectivity search provides rapid insights into how two biomedical entities are related.

Author

Himmelstein DS, Zietz M, Rubinetti V, Kloster K, Heil BJ, Alquaddoomi F, Hu D, Nicholson DN, Hao Y, Sullivan BD, Nagle MW, and Greene CS

Abstract

Hetnets, short for "heterogeneous networks", contain multiple node and relationship types and offer a way to encode biomedical knowledge. One such example, Hetionet connects 11 types of nodes - including genes, diseases, drugs, pathways, and anatomical structures - with over 2 million edges of 24 types. Previous work has demonstrated that supervised machine learning methods applied to such networks can identify drug repurposing opportunities. However, a training set of known relationships does not exist for many types of node pairs, even when it would be useful to examine how nodes of those types are meaningfully connected. For example, users may be curious not only how metformin is related to breast cancer, but also how the GJA1 gene might be involved in insomnia. We developed a new procedure, termed hetnet connectivity search, that proposes important paths between any two nodes without requiring a supervised gold standard. The algorithm behind connectivity search identifies types of paths that occur more frequently than would be expected by chance (based on node degree alone). We find that predictions are broadly similar to those from previously described supervised approaches for certain node type pairs. Scoring of individual paths is based on the most specific paths of a given type. Several optimizations were required to precompute significant instances of node connectivity at the scale of large knowledge graphs. We implemented the method on Hetionet and provide an online interface at https://het.io/search . We provide an open source implementation of these methods in our new Python package named hetmatpy .

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results