通常一个卷积神经网络架构包含两个可以通过训练产生的非线性卷积层,两个固定的子采样层和一个全连接层,隐藏层的数量一般至少在5个以上。 CNN的架构设计是受到生物学家Hubel和Wiesel的动物视觉模型启发而发明的,尤其是模拟动物视觉皮层V1层和V2层中简单细胞(Simple Cell)和复杂细胞(Complex Cell)在视觉系统的功能。
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-...
在灵活且易于使用的软件的支持下,DeepCCI 可以提供一站式解决方案,以发现有意义的细胞间相互作用并从 scRNA-seq 数据构建 CCI 网络。 该研究以「DeepCCI: a deep learning framework for identifying cell–cell interactions from single-cell RNA sequencing data」为题,于 2023 年 9 月 23 日发布在《Bioinformat...
Inspired by recent approaches for natural language processing and computer vision, we developed Annotatability, a framework that analyzes deep neural network training dynamics to interpret pre-annotated single-cell and spatial omics data. Annotatability identified erroneous annotations and ambiguous cell ...
Advances in the artificial neural network have made machine learning techniques increasingly more important in image analysis tasks. Recently, convolutional neural networks (CNN) have been applied to the problem of cell segmentation from microscopy images. However, previous methods used a supervised traini...
Using these uniformly constructed cross-species landscapes, we developed a deep-learning-based strategy, Nvwa, to predict gene expression and identify regulatory sequences at the single-cell level. We systematically compared cell-type-specific transcription factors to reveal conserved genetic regulation in...
deepcell-tfis a deep learning library for single-cell analysis of biological images. It is written in Python and built usingTensorFlow 2. This library allows users to apply pre-existing models to imaging data as well as to develop new deep learning models for single-cell analysis. This library...
Deep learning infrastructure: With the great increase in the number of single cells, classical methods [60,61] cannot effectively enjoy the benefit from big single-cell data, while deep learning has been proven to be effective. Furthermore, deep learning techniques are also good at handling high...
Single-cell sequencing provides detailed insights into biological processes including cell differentiation and identity. While providing deep cell-specific information, the method suffers from technical constraints, most notably a limited number of expressed genes per cell, which leads to suboptimal clustering...
DeepFLEX: Deep learning-based single-cell analysis pipeline for FLuorescence multiplEX imaging - perlfloccri/DeepFLEX