Exploring combinations of dimensionality reduction, transfer learning, and regularization methods for predicting binary phenotypes with transcriptomic data

Abstract Background Numerous transcriptomic-based models have been developed to predict or understand the fundamental mechanisms driving biological phenotypes. However, few models have successfully transitioned into clinical practice due to challenges associated with generalizability and interpretab...

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Auteurs principaux:	S. R. Oshternian, S. Loipfinger, A. Bhattacharya, R. S. N. Fehrmann
Format:	Artigo
Langue:	Inglês
Publié:	BMC 2024-04-01
Collection:	BMC Bioinformatics
Sujets:	Dimensionality reduction Transfer learning Predictive modeling Transcriptomic data Independent component analysis Autoencoder
Accès en ligne:	https://doi.org/10.1186/s12859-024-05795-6
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