Malepathirana, T and Senanayake, D and Gautam, V and Engel, M and Balez, R and Lovelace, MD and Sundaram, G and Heng, B and Chow, S and Marquis, C and Guillemin, GJ and Brew, B and Jagadish, C and Ooi, L and Halgamuge, S (2024) Visualization of incrementally learned projection trajectories for longitudinal data. In: Scientific Reports, 14 (1).
|
PDF
Sci_Rep_Vol_14_2024_13558.pdf - Published Version Download (3MB) | Preview |
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. © The Author(s) 2024.
| Item Type: | Journal Article |
|---|---|
| Publication: | Scientific Reports |
| Publisher: | Nature Research |
| Additional Information: | The copyright for this article belongs to the Authors. |
| Keywords: | Alzheimer disease; brain; human; longitudinal study; machine learning; metabolism; organoid; physiology, Alzheimer Disease; Brain; Humans; Longitudinal Studies; Machine Learning; Organoids |
| Department/Centre: | Division of Interdisciplinary Sciences > Centre for Nano Science and Engineering |
| Date Deposited: | 31 Jul 2024 05:07 |
| Last Modified: | 31 Jul 2024 05:08 |
| URI: | http://eprints.iisc.ac.in/id/eprint/85687 |
Actions (login required)
 |
View Item |
