Latent Feature Representations for Human Gene Expression Data Improve Phenotypic Predictions
Yannis Pantazis, Christos Tselas, Kleanthi Lakiotaki, Vincenzo Lagani, Ioannis Tsamardinos
The paper constructs low-dimensional latent feature spaces of the human transcriptome using dimensionality reduction techniques and evaluates their ability to reconstruct data and improve phenotypic predictions.
IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · Dec 16, 2020Peer-reviewedBioinformatics