Article

Latent Feature Representations for Human Gene Expression Data Improve Phenotypic Predictions

2020-10-16

Abstract excerpt

High-throughput technologies such as microarrays and RNA-sequencing (RNA-seq) allow to precisely quantify transcriptomic profiles, generating datasets that are inevitably high-dimensional. In this work, we investigate whether the whole human transcriptome can be represented in a compressed, low dimensional latent space without loosing relevant information. We thus constructed low-dimensional latent feature spaces...

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Literature Corpus work: 1cd8005b-f58d-55b8-bf97-ff2781a589ac
DOI: 10.1101/2020.10.15.340802

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