Posts by Collection

publications

Model-based Feature Selection and Clustering of RNA-seq Data for Unsupervised Subtype Discovery

Published in Annals of Applied Statistics, 2020

Simultaneous variable selection and clustering of samples, with adjustment for technical factors like differences in sequencing depth, and covariates like batch.

Recommended citation: D.K. Lim, N.U. Rashid, J.G. Ibrahim (2020). "Model-based Feature Selection and Clustering of RNA-seq Data for Unsupervised Subtype Discovery" Annals of Applied Statistics. http://dx.doi.org/10.1214/20-AOAS1407

Unsupervised Imputation of Non-ignorably Missing Data Using Importance-Weighted Autoencoders

Published in arXiv, 2021

Imputation of missing not at random data using a deeply-learned autoencoder.

Recommended citation: D.K. Lim, N.U. Rashid, J.B. Oliva, J.G. Ibrahim (2022). "Unsupervised Imputation of Non-ignorably Missing Data Using Importance-Weighted Autoencoders" arXiv https://arxiv.org/abs/2101.07357

Deeply-Learned Generalized Linear Models with Missing Data

Published in Journal of Computational and Graphical Statistics, 2023

Supervised Learning in the presence of missing not at random data using a deeply-learned neural networks.

Recommended citation: D.K. Lim, N.U. Rashid, J.B. Oliva, J.G. Ibrahim (2023). "Deeply-Learned Generalized Linear Models with Missing Data" Journal of Computational and Graphical Statistics https://www.tandfonline.com/doi/full/10.1080/10618600.2023.2276122

David Lim

Posts by Collection

publications

Model-based Feature Selection and Clustering of RNA-seq Data for Unsupervised Subtype Discovery

Unsupervised Imputation of Non-ignorably Missing Data Using Importance-Weighted Autoencoders

Deeply-Learned Generalized Linear Models with Missing Data

software

FSCseq

NIMIWAE

dlglm

talks

Unsupervised Clustering and Variable Selection for RNA-Seq Data

FSCseq: Simultaneous Feature Selection and Clustering of RNA-Seq Data

Missing Data in Deep Learning

Handling Missing Electronic Health Records Data Using Importance-Weighted Autoencoders