Abstract
Biomedical datasets that aim to collect diverse phenotypic and genomic data across large numbers of individuals are plagued by the large fraction of missing data The ability to accurately impute or “fill-in” missing entries in these datasets is critical to a number of downstream applications.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
An, U., Cai, N., Dahl, A., Sankararaman, S. (2022). AutoComplete: Deep Learning-Based Phenotype Imputation for Large-Scale Biomedical Data. In: Pe'er, I. (eds) Research in Computational Molecular Biology. RECOMB 2022. Lecture Notes in Computer Science(), vol 13278. Springer, Cham. https://doi.org/10.1007/978-3-031-04749-7_38
Download citation
DOI: https://doi.org/10.1007/978-3-031-04749-7_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-04748-0
Online ISBN: 978-3-031-04749-7
eBook Packages: Computer ScienceComputer Science (R0)