30 citations · 76 across the 10 of their papers we have counts for
10 papers
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…
Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator
Siamak Zamani Dadaneh, Shahin Boluki, Mingzhang Yin +2
Semantic hashing has become a crucial component of fast similarity search in many large-scale information retrieval systems, in particular, for text data. Variational auto-encoders…
Learnable Bernoulli Dropout for Bayesian Deep Learning
Shahin Boluki, Randy Ardywibowo, Siamak Zamani Dadaneh +2
In this work, we propose learnable Bernoulli dropout (LBD), a new model-agnostic dropout scheme that considers the dropout rates as parameters jointly optimized with other model pa…
ARSM Gradient Estimator for Supervised Learning to Rank
Siamak Zamani Dadaneh, Shahin Boluki, Mingyuan Zhou +1
We propose a new model for supervised learning to rank. In our model, the relevance labels are assumed to follow a categorical distribution whose probabilities are constructed base…
Bayesian Gamma-Negative Binomial Modeling of Single-Cell RNA Sequencing Data
Siamak Zamani Dadaneh, Paul de Figueiredo, Sing-Hoi Sze +2
Background: Single-cell RNA sequencing (scRNA-seq) is a powerful profiling technique at the single-cell resolution. Appropriate analysis of scRNA-seq data can characterize molecula…
Optimal Clustering with Missing Values
Shahin Boluki, Siamak Zamani Dadaneh, Xiaoning Qian +1
Missing values frequently arise in modern biomedical studies due to various reasons, including missing tests or complex profiling technologies for different omics measurements. Mis…
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…
Sequential Sampling for Optimal Bayesian Classification of Sequencing Count Data
Ariana Broumand, Siamak Zamani Dadaneh
High throughput technologies have become the practice of choice for comparative studies in biomedical applications. Limited number of sample points due to sequencing cost or access…
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…
BNP-Seq: Bayesian Nonparametric Differential Expression Analysis of Sequencing Count Data
Siamak Zamani Dadaneh, Xiaoning Qian, Mingyuan Zhou
We perform differential expression analysis of high-throughput sequencing count data under a Bayesian nonparametric framework, removing sophisticated ad-hoc pre-processing steps co…