How has the focus of research papers on a given disease changed over time? Identifying the papers at the cusps of change can help highlight the emergence of a new topic or a change in the direction of research. We present a generally applicable unsupervised approach to this question based on semantic changepoints within a given collection of research papers. We illustrate the approach by a range of examples based on a nascent corpus of literature on COVID-19 as well as subsets of papers from PubMed on the World Health Organization list of neglected tropical diseases. The software is freely available at: https://github.com/pdddinakar/SemanticChangepointDetection.
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Front Physiol
April 2023
Department of Informatics, University of Oslo, Oslo, Norway.
The development of compact and energy-efficient wearable sensors has led to an increase in the availability of biosignals. To effectively and efficiently analyze continuously recorded and multidimensional time series at scale, the ability to perform meaningful unsupervised data segmentation is an auspicious target. A common way to achieve this is to identify change-points within the time series as the segmentation basis.
View Article and Find Full Text PDFBioData Min
May 2023
Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA.
While we often think of words as having a fixed meaning that we use to describe a changing world, words are also dynamic and changing. Scientific research can also be remarkably fast-moving, with new concepts or approaches rapidly gaining mind share. We examined scientific writing, both preprint and pre-publication peer-reviewed text, to identify terms that have changed and examine their use.
View Article and Find Full Text PDFPac Symp Biocomput
March 2021
Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA 94720, USA,
How has the focus of research papers on a given disease changed over time? Identifying the papers at the cusps of change can help highlight the emergence of a new topic or a change in the direction of research. We present a generally applicable unsupervised approach to this question based on semantic changepoints within a given collection of research papers. We illustrate the approach by a range of examples based on a nascent corpus of literature on COVID-19 as well as subsets of papers from PubMed on the World Health Organization list of neglected tropical diseases.
View Article and Find Full Text PDFMethods Mol Biol
June 2019
German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
In this chapter, we review the problem of network inference from time-course data, focusing on a class of graphical models known as dynamic Bayesian networks (DBNs). We discuss the relationship of DBNs to models based on ordinary differential equations, and consider extensions to nonlinear time dynamics. We provide an introduction to time-varying DBN models, which allow for changes to the network structure and parameters over time.
View Article and Find Full Text PDFNeuropsychology
November 2015
Department of Neurology, Washington University School of Medicine in St. Louis.
Objective: To describe how practice effects influence cognitive trajectories and determine if a reduction in practice effects is a potential marker of Stage-III preclinical Alzheimer's disease (AD).
Method: Participants included 263 older adults who were cognitively normal at baseline (i.e.
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