Search Results for author:
Found 7 papers, 1 papers with code
SimCD: Simultaneous Clustering and Differential expression analysis for single-cell transcriptomic data
We develop a new method -- SimCD -- that explicitly models cell heterogeneity and dynamic differential changes in one unified hierarchical gamma-negative binomial (hGNB) model, allowing simultaneous cell clustering and differential expression analysis for scRNA-seq data.
Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator
Semantic hashing has become a crucial component of fast similarity search in many large-scale information retrieval systems, in particular, for text data.
Learnable Bernoulli Dropout for Bayesian Deep Learning
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 parameters.
ARSM Gradient Estimator for Supervised Learning to Rank
Learning-to-rank methods can generally be categorized into pointwise, pairwise, and listwise approaches.
Optimal Clustering with Missing Values
Missing values frequently arise in modern biomedical studies due to various reasons, including missing tests or complex profiling technologies for different omics measurements.
Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data
Second, compared to the number of involved molecules and system complexity, the number of available samples for studying complex disease, such as cancer, is often limited, especially considering disease heterogeneity.
Differential Expression Analysis of Dynamical Sequencing Count Data with a Gamma Markov Chain
Next-generation sequencing (NGS) to profile temporal changes in living systems is gaining more attention for deriving better insights into the underlying biological mechanisms compared to traditional static sequencing experiments.
