生物信息遇上Deep learning(13): scVAE: Variational auto-encoders for single-cell gene expression data 背景 使用改进的变分自编码分析单细胞RNA数据,预估对应的基因表达水平和细胞的隐式表达 motivation:目前大多的计算框架流程复杂,需要专业分析,而且大多不基于原始raw count数据,生成式模型会更加适合RNA-seq数据生...
Deep learningRNA-SeqPersonalized medicineMachine LearningBiomarkers discoveryDeep learning models are currently being applied in several areas with great success. However, their application for the analysis of high-throughput sequencing data remains a challenge for the research community due to the fact ...
未来Deep learning将会成为生信的标准工具,这是大势所趋,不可阻挡。 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 ...
2021年8月10日,深圳华大生命科学研究院精准健康研究所智能算法团队在知名学术杂志《遗传学前沿》(Frontiers in Genetics)在线发表了题为“deepMNN: Deep Learning-Based Single-Cell RNA Sequencing Data Batch Correction Using Mutual Nearest Neighbors”的研究论文,文章提出了一种新的基于深度学习模型进行单细胞RNA测序...
DARTS leverages public RNA-seq big data to provide a knowledge base of splicing regulation via deep learning, thereby helping researchers better characterize alternative splicing using RNA-seq datasets even with modest coverage.doi:10.1038/s41592-019-0351-9Zhang, Zijun...
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-...
Single-cell RNA sequencing Single-cell sequencing Deep learning Deep neural network Artificial intelligence Introduction Since the first single-cell RNA sequencing (scRNA-seq) paper in 2009 [1] and subsequent designation of “method of the year” a few years after [2], [3], [4], [5], [6...
Here, we present HE2RNA, a deep-learning algorithm specifically customized for the prediction of gene expression from WSI (Fig.1). For training our model, we collected WSIs and their corresponding RNA-Seq data from The Cancer Genome Atlas (TCGA) public database. We then investigated how HE2...
为了捕捉这种信息,8月5日Nature Methods的研究报道“Geometric deep learning of protein–DNA binding specificity”,开发了结合特异性深度预测器(DeepPBS),这是一种几何深度学习模型,旨在从蛋白质-DNA结构中预测结合特异性。DeepPBS可以应用...
DeepCCI是一种基于图卷积网络(GCN)的深度学习框架,用于从scRNA-seq数据中鉴定CCIs。为了从scRNA-seq数据中一站式探索细胞之间的相互作用,DeepCCI提供了两个深度学习模型:(i)用于细胞聚类的基于GCN的无监督模型,以及(ii)用于CCI识别的基于GCN的监督模型。DeepCCI通过利用scRNA-Seq数据中异质性细胞的潜在复杂基因表达模...