RegenX: an NLP recommendation engine for neuroregeneration topics over time.

Autor:	Khosla S; New York University, Center for Data Science, New York, NY, USA., Abdelrahman L; Department of Ophthalmology & Miami Integrative Metabolomics Research Center, University of Miami, Bascom Palmer Eye Institute, Miami, FL, USA., Johnson J; Department of Marketing, University of Miami, Miami Herbert Business School, Miami, FL, USA., Samarah M; Carroll University, Wisconsin, WI, USA., Bhattacharya SK; Department of Ophthalmology & Miami Integrative Metabolomics Research Center, University of Miami, Bascom Palmer Eye Institute, Miami, FL, USA.
Jazyk:	angličtina
Zdroj:	Annals of eye science [Ann Eye Sci] 2022 Mar; Vol. 7. Date of Electronic Publication: 2022 Mar 15.
DOI:	10.21037/aes-21-29
Abstrakt:	Background: In this investigation, we explore the literature regarding neuroregeneration from the 1700s to the present. The regeneration of central nervous system neurons or the regeneration of axons from cell bodies and their reconnection with other neurons remains a major hurdle. Injuries relating to war and accidents attracted medical professionals throughout early history to regenerate and reconnect nerves. Early literature till 1990 lacked specific molecular details and is likely provide some clues to conditions that promoted neuron and/or axon regeneration. This is an avenue for the application of natural language processing (NLP) to gain actionable intelligence. Post 1990 period saw an explosion of all molecular details. With the advent of genomic, transcriptomics, proteomics, and other omics-there is an emergence of big data sets and is another rich area for application of NLP. How the neuron and/or axon regeneration related keywords have changed over the years is a first step towards this endeavor. Methods: Specifically, this article curates over 600 published works in the field of neuroregeneration. We then apply a dynamic topic modeling algorithm based on the Latent Dirichlet allocation (LDA) algorithm to assess how topics cluster based on topics. Results: Based on how documents are assigned to topics, we then build a recommendation engine to assist researchers to access domain-specific literature based on how their search text matches to recommended document topics. The interface further includes interactive topic visualizations for researchers to understand how topics grow closer and further apart, and how intra-topic composition changes over time. Conclusions: We present a recommendation engine and interactive interface that enables dynamic topic modeling for neuronal regeneration. Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://aes.amegroups.com/article/view/10.21037/aes-21-29/coif). SKB serves as an unpaid editorial board member of Annals of Eye Science from August 2020 to July 2022. SK reports that their research is mostly online using public databases. However, infrastructure such as basic software including zoom meetings were supported by the University of Miami. Personal and software has support from NIH grant EY14801 and RPB unrestricted grants to University of Miami for research. LA reports that the research is mostly online using public databases, while meeting software (e.g., Zoom) was sponsored by the University of Miami. JJ reports that there are no conflicts of interest with regard to funding sources, meetings and travels reported in his COI form. MS reports that their research is mostly online using public databases. However, infrastructure such as basic software including zoom meetings were supported by the University of Miami. Personal and software has support from NIH grant EY14801 and RPB unrestricted grants to University of Miami for research. SKB reports that their research is mostly online using public databases. However, infrastructure such as basic software including zoom meetings were supported by the University of Miami. Personal and software has support from NIH grant EY14801 and RPB unrestricted grants to University of Miami for research.
Databáze:	MEDLINE
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