62 citations · 154 across the 13 of their papers we have counts for
13 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…
Bayesian Graph Contrastive Learning
Arman Hasanzadeh, Mohammadreza Armandpour, Ehsan Hajiramezanali +3
Contrastive learning has become a key component of self-supervised learning approaches for graph-structured data. Despite their success, existing graph contrastive learning methods…
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…
Semi-Implicit Graph Variational Auto-Encoders
Arman Hasanzadeh, Ehsan Hajiramezanali, Nick Duffield +3
Semi-implicit graph variational auto-encoder (SIG-VAE) is proposed to expand the flexibility of variational graph auto-encoders (VGAE) to model graph data. SIG-VAE employs a hierar…
A Novel Maneuvering Target Tracking Approach by Stochastic Volatility GARCH Model
Ehsan Hajiramezanali, Seyyed Hamed Fouladi, Hamidreza Amindavar
In this paper, we introduce a new single model maneuvering target tracking approach using stochastic differential equation (SDE) based on GARCH volatility. The traditional input es…
Non-Linear Non-Stationary Heteroscedasticity Volatility for Tracking of Jump Processes
Seyyed Hamed Fouladi, Ehsan Hajiramezanali
In this paper, we introduce a new jump process modeling which involves a particular kind of non-Gaussian stochastic processes with random jumps at random time points. The main goal…
Scalable optimal Bayesian classification of single-cell trajectories under regulatory model uncertainty
Ehsan Hajiramezanali, Mahdi Imani, Ulisses Braga-Neto +2
Single-cell gene expression measurements offer opportunities in deriving mechanistic understanding of complex diseases, including cancer. However, due to the complex regulatory mac…
Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data
Ehsan Hajiramezanali, Siamak Zamani Dadaneh, Alireza Karbalayghareh +2
Precision medicine aims for personalized prognosis and therapeutics by utilizing recent genome-scale high-throughput profiling techniques, including next-generation sequencing (NGS…
Differential Expression Analysis of Dynamical Sequencing Count Data with a Gamma Markov Chain
Ehsan Hajiramezanali, Siamak Zamani Dadaneh, Paul de Figueiredo +3
Next-generation sequencing (NGS) to profile temporal changes in living systems is gaining more attention for deriving better insights into the underlying biological mechanisms comp…