这篇论文研究的是Single-cell RNA sequencing (scRNA-seq) denoising, 也就是单细胞RNA测序的降噪,由于数据扩增和数据丢失等问题,会干扰scRNA-seq的数据分析,因此需要有降噪技术用于稀疏的scRNA-seq数据,作者提出了一种deep count autoencoder network (DCA),通过negative binomial noise model with or without zero-...
A deep count autoencoder network to denoise scRNA-seq data and remove the dropout effect by taking the count structure, overdispersed nature and sparsity of the data into account using a deep autoencoder with zero-inflated negative binomial (ZINB) loss function. ...
so scalable denoising methods for increasingly large but sparse scRNA-seq data are needed. We propose a deep count autoencoder network (DCA) to denoise scRNA-seq datasets. DCA takes the count distribution, overdispersion and sparsity of the data into account using a negative binomial noise model ...
Autoencoder:机器学习中的自动编码器,这篇文章里面用的是去噪编码器,坊间称之为denoise autoencoder(DAE),在sc-RNAseq中除去dropout的噪声是非常理想的一种模型。 Therefore,这篇文章已经发表在了NC的18年预印本上,证明其方法和文章质量很是不错。 基本原理of DAE: ###measurement noise from dropout events,moves...
我目前在研究的MIRA就是使用了Autoencoder,这个已经在单细胞领域非常成熟了。【清一色NC灌水】 降噪- Single-cell RNA-seq denoising using a deep count autoencoder 空间- Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder ...
Hence, this paper presents an occupancy detection approach for detecting the person's count in the room or building using the proposed Chaotic Whale Spider Monkey (ChaoWSM) + Deep stacked autoencoder. The input data are initially fed to the pre-processing step. The pre-processing is done ...
Thus, a small subset of pixels can be sampled from 3D MSI data for training the network, which can rapidly embed a large number of pixels into a single embedding space. We used an autoencoder in conjunction with UMAP, of which the encoder is trained to minimize UMAP loss and the decoder...
A Note About the Role of Autoencoders Aswe previously covered inChapter 3, autoencoders fundamental structures in deep networks because they’re often used as part of larger networks. Like many other networks, they serve that role and then are used as a standalone network, as well. ...
《Deep Learning, The Curse of Dimensionality, and Autoencoders》 介绍: 讨论深度学习自动编码器如何有效应对维数灾难,国内翻译 《Advanced Optimization and Randomized Methods》 介绍: CMU的优化与随机方法课程,由A. Smola和S. Sra主讲,优化理论是机器学习的基石,值得深入学习 国内云(视频) 《CS231n: Convolutio...
The proposed method achieves a detection score of 0.98 on the Case Western Reserve University (CWRU) bearing dataset, which is 0.23 higher than the approach based on the LSTM autoencoder network for novel class recognition and fault diagnosis, and is 0.05 higher than the SAE network-based ...