Intrinsic dimensionality of deep learning representations reveals cell death-associated heterogeneity in Parkinsons disease iPSC-derived neurons
Choi, M. L.; Kang, S.; Park, J.
Show abstract
As AI is increasingly used to stratify heterogeneous Parkinsons disease, it is essential to determine whether learned representations preserve disease-relevant variation within diagnostic or genetic groups. In iPSC-derived neurons from three familial PD cases, deep learning representations encoded an independent measure of cell death, and their intrinsic dimensionality increased with death burden. Latent dimensionality offers a way to examine heterogeneity before AI-derived clusters are interpreted as disease subtypes.
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