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Visualization of incrementally learned projection trajectories for longitudinal data.
- Source :
-
Scientific reports [Sci Rep] 2024 Jun 12; Vol. 14 (1), pp. 13558. Date of Electronic Publication: 2024 Jun 12. - Publication Year :
- 2024
-
Abstract
- Longitudinal studies that continuously generate data enable the capture of temporal variations in experimentally observed parameters, facilitating the interpretation of results in a time-aware manner. We propose IL-VIS (incrementally learned visualizer), a new machine learning pipeline that incrementally learns and visualizes a progression trajectory representing the longitudinal changes in longitudinal studies. At each sampling time point in an experiment, IL-VIS generates a snapshot of the longitudinal process on the data observed thus far, a new feature that is beyond the reach of classical static models. We first verify the utility and correctness of IL-VIS using simulated data, for which the true progression trajectories are known. We find that it accurately captures and visualizes the trends and (dis)similarities between high-dimensional progression trajectories. We then apply IL-VIS to longitudinal multi-electrode array data from brain cortical organoids when exposed to different levels of quinolinic acid, a metabolite contributing to many neuroinflammatory diseases including Alzheimer's disease, and its blocking antibody. We uncover valuable insights into the organoids' electrophysiological maturation and response patterns over time under these conditions.<br /> (© 2024. The Author(s).)
Details
- Language :
- English
- ISSN :
- 2045-2322
- Volume :
- 14
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Scientific reports
- Publication Type :
- Academic Journal
- Accession number :
- 38866809
- Full Text :
- https://doi.org/10.1038/s41598-024-63511-z