Khosla Shaan, Abdelrahman Leila, Johnson Joseph, Samarah Mohammad, Bhattacharya Sanjoy K
New York University, Center for Data Science, New York, NY, USA.
Department of Ophthalmology & Miami Integrative Metabolomics Research Center, University of Miami, Bascom Palmer Eye Institute, Miami, FL, USA.
Ann Eye Sci. 2022 Mar;7. doi: 10.21037/aes-21-29. Epub 2022 Mar 15.
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.
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.
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.
We present a recommendation engine and interactive interface that enables dynamic topic modeling for neuronal regeneration.
在本研究中，我们探讨了从18世纪到现在有关神经再生的文献。中枢神经系统神经元的再生，或者细胞体轴突的再生以及它们与其他神经元的重新连接仍然是一个主要障碍。与战争和事故相关的损伤在早期历史中吸引了医学专业人员来促进神经的再生和重新连接。直到1990年的早期文献缺乏具体的分子细节，可能为促进神经元和/或轴突再生的条件提供了一些线索。这是自然语言处理（NLP）应用以获取可操作情报的一个途径。1990年之后，所有分子细节大量涌现。随着基因组学、转录组学、蛋白质组学和其他组学的出现，出现了大量数据集，这也是NLP应用的另一个丰富领域。多年来神经元和/或轴突再生相关关键词如何变化是朝着这一努力迈出的第一步。
具体而言，本文整理了神经再生领域600多篇已发表的著作。然后我们应用基于潜在狄利克雷分配（LDA）算法的动态主题建模算法来评估主题如何基于主题进行聚类。
根据文档如何分配到主题，我们随后构建了一个推荐引擎，以帮助研究人员根据他们的搜索文本与推荐文档主题的匹配程度来获取特定领域的文献。该界面还包括交互式主题可视化，以便研究人员了解主题如何变得更紧密或更疏远，以及主题内的组成如何随时间变化。
我们展示了一个推荐引擎和交互式界面，可实现神经元再生的动态主题建模。
