A generalized and versatile framework to train and evaluate autoencoders for biological representation learning and beyond: AUTOENCODIX

Insights and discoveries in complex biological systems, e.g. for personalized medicine, are gained by the combination of large, feature-rich and high-dimensional data with powerful computational methods uncovering patterns and relationships. In recent years, autoencoders, a family of deep learning-b...

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Bibliographic Details

Published in:	bioRxiv
Main Authors:	Joas, Maximilian, Jurenaite, Neringa, Praščević, Dušan, Scherf, Nico, Ewald, Jan
Format:	Paper
Language:	English
Published:	Cold Spring Harbor Cold Spring Harbor Laboratory Press 20.12.2024 Cold Spring Harbor Laboratory
Edition:	1.1
Subjects:	Bioinformatics Deep learning Embedding Medical research Ontology Precision medicine Sensory integration
ISSN:	2692-8205, 2692-8205
Online Access:	Get full text
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