Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Dadaneh, Siamak Zamani"'
Autor:
Niyakan, Seyednami, Hajiramezanali, Ehsan, Boluki, Shahin, Dadaneh, Siamak Zamani, Qian, Xiaoning
Single-Cell RNA sequencing (scRNA-seq) measurements have facilitated genome-scale transcriptomic profiling of individual cells, with the hope of deconvolving cellular dynamic changes in corresponding cell sub-populations to better understand molecula
Externí odkaz:
http://arxiv.org/abs/2104.01512
Publikováno v:
Uncertainty in Artificial Intelligence Conference (UAI) 2020
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 (VAEs) with binary latent variables as hashing codes provide state-of
Externí odkaz:
http://arxiv.org/abs/2005.10477
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. By probabilistic modeling of Bernoulli dropout, our method e
Externí odkaz:
http://arxiv.org/abs/2002.05155
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 based on a scoring function. We optimize the training objective with respe
Externí odkaz:
http://arxiv.org/abs/1911.00465
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 molecular heterogeneity and shed light into the underlying cellular process to
Externí odkaz:
http://arxiv.org/abs/1908.00650
Missing values frequently arise in modern biomedical studies due to various reasons, including missing tests or complex profiling technologies for different omics measurements. Missing values can complicate the application of clustering algorithms, w
Externí odkaz:
http://arxiv.org/abs/1902.09694
Autor:
Hajiramezanali, Ehsan, Dadaneh, Siamak Zamani, Karbalayghareh, Alireza, Zhou, Mingyuan, Qian, Xiaoning
Precision medicine aims for personalized prognosis and therapeutics by utilizing recent genome-scale high-throughput profiling techniques, including next-generation sequencing (NGS). However, translating NGS data faces several challenges. First, NGS
Externí odkaz:
http://arxiv.org/abs/1810.09433
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 to organisms of interest necessitates the development of efficient sa
Externí odkaz:
http://arxiv.org/abs/1807.05920
Autor:
Hajiramezanali, Ehsan, Dadaneh, Siamak Zamani, de Figueiredo, Paul, Sze, Sing-Hoi, Zhou, Mingyuan, Qian, Xiaoning
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. Nonetheless, the ma
Externí odkaz:
http://arxiv.org/abs/1803.02527
We perform differential expression analysis of high-throughput sequencing count data under a Bayesian nonparametric framework, removing sophisticated ad-hoc pre-processing steps commonly required in existing algorithms. We propose to use the gamma (b
Externí odkaz:
http://arxiv.org/abs/1608.03991