17 citations · 22 across the 5 of their papers we have counts for
11 papers
MoReL: Multi-omics Relational Learning
Arman Hasanzadeh, Ehsan Hajiramezanali, Nick Duffield +1
Multi-omics data analysis has the potential to discover hidden molecular interactions, revealing potential regulatory and/or signal transduction pathways for cellular processes of…
SimCD: Simultaneous Clustering and Differential expression analysis for single-cell transcriptomic data
Seyednami Niyakan, Ehsan Hajiramezanali, Shahin Boluki +2
Single-Cell RNA sequencing (scRNA-seq) measurements have facilitated genome-scale transcriptomic profiling of individual cells, with the hope of deconvolving cellular dynamic chang…
BayReL: Bayesian Relational Learning for Multi-omics Data Integration
Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield +2
High-throughput molecular profiling technologies have produced high-dimensional multi-omics data, enabling systematic understanding of living systems at the genome scale. Studying…
Bayesian Graph Neural Networks with Adaptive Connection Sampling
Arman Hasanzadeh, Ehsan Hajiramezanali, Shahin Boluki +4
We propose a unified framework for adaptive connection sampling in graph neural networks (GNNs) that generalizes existing stochastic regularization methods for training GNNs. The p…
Semi-Implicit Stochastic Recurrent Neural Networks
Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield +3
Stochastic recurrent neural networks with latent random variables of complex dependency structures have shown to be more successful in modeling sequential data than deterministic d…
Variational Graph Recurrent Neural Networks
Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield +3
Representation learning over graph structured data has been mostly studied in static graph settings while efforts for modeling dynamic graphs are still scant. In this paper, we dev…